Determining prospective risk of heart failure hospitalization

ABSTRACT

A method of operation of a medical device system for determining prospective heart failure hospitalization risk. The method includes measuring one or more data observations via one or more electrodes of an implanted medical device disposed in a patient&#39;s body. The data observations are stored into memory of the implantable medical device of a patient. The data observations are transmitted to an external device. The processor of the external device parses the data observations into one or more evaluation periods. Using the number of observations in one or more evaluation periods, a look up table, stored into memory of the external device, is accessed. The look up table associates prospective heart failure hospitalization risk with the data observations noted in the evaluation period. One or more embodiments involve a weighted prospective heart failure hospitalization risk for the set of evaluation periods. The prospective heart failure hospitalization is then displayed on the graphical user interface.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/024,285, filed on Jul. 14, 2014. The disclosure of the aboveapplication is incorporated herein by reference in its entirety. Thisapplication further claims the benefit of U.S. Provisional ApplicationNo. 62/037,895, filed on Aug. 15, 2014. The disclosure of the aboveapplication is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to medical devices, and, moreparticularly, to medical devices that monitor cardiac health.

BACKGROUND

Chronic heart failure (HF) occurs when a heart is unable to consistentlypump blood at an adequate rate in response to the filling pressure. Toimprove the ability of the heart to pump blood, congestive heart failurepatients, classified as having New York Heart Association (NYHA) classstatus of II to IV HF, may require implantable medical devices (IMDs)such as implantable cardioverter defibrillators (ICDs) and cardiacresynchronization devices with defibrillation capability (CRT-Ds).Despite using IMDs to improve heart function, some HF patients mayrequire hospitalization. Global health care systems incur billions ofdollars each year due to heart failure hospitalizations (HFHs).Identifying patients at risk of HFH to enable timely intervention andprevent expensive hospitalization remains a challenge. Implantablecardioverter defibrillators (ICDs) and cardiac resynchronization deviceswith defibrillation capability (CRT-Ds) are configured to acquire datafor a variety of diagnostic metrics that change with HF status andcollectively have the potential to signal an increasing risk of HFH.Diagnostic parameter data collected by IMDs include activity, day andnight heart rate, atrial tachycardia/atrial fibrillation (AT/AF) burden,mean rate during AT/AF, percent CRT pacing, number of shocks, andintrathoracic impedance. Additionally, preset or programmable thresholdsfor diagnostic metrics, when crossed, trigger a notification, referredto as device observation. Each device observation is recorded in an IMDreport.

One conventional method for predicting HFH risk is US pregrantpublication No. 2012/0253207 A1, entitled Heart Failure Monitoring, toSarkar et al. Sarkar et al. is directed to a post-discharge period inwhich the IMD is interrogated remotely through wireless transmission toevaluate the prognosis of the patient using device diagnostics. Forexample, an evaluation can be performed during a 7 day period postdischarge such that a determination is made as whether the patient had1-6 days of AF burden>6 hrs, poor rate control (i.e. 1 day of AF>6 hrsand rate>90 bpm), a fluid index greater than 60 or 100 ohm-days, nightheart rate>85 bpm, heart rate variability less than or equal to 40 ms,ventricular tachycardia, or % CRT pacing<90%. If any two of the listedparameters were met, the patient is considered high risk for are-admission and is designated for post discharge care (e.g. nurse callor treatment modifications). If no criterion is met, the patient isconsidered at lower risk for HFH and less attention is provided to thatpatient. While Sarkar et al. provides useful information as tocalculating the risk of HFH, it is desirable to provide gradations ofHFH risk. Additionally, it is also desirable to provide develop a methodthat simplifies the HFH risk calculation without regard as to whethertwo different listed parameters were triggered.

Another method for estimating HFH risk is disclosed in a riskstratification study by Martin R. Cowie et al., Development AndValidation Of An Integrated Diagnostic Algorithm Derived From ParametersMonitored In Implantable Devices For Identifying Patients At Risk ForHeart Failure Hospitalization In An Ambulatory Setting Which DisclosedThat Various IMD Diagnostics Variables Could Be Combined For ThePrevious 30-Days Using A Heuristic Approach To Assess Patient HF Risk InThe Next 30 Days, European Heart Journal (Aug. 14, 2013) (hereinafterreferred to as the EHJ article).

Yet another method involves U.S. Pat. No. 8,768,718 B2 to Cazares et al.Cazares et al. uses between-patient comparisons for risk stratificationof future heart failure decompensation. Current patient data iscollected by a patient monitoring device. A reference group related tothe patient is determined. A reference group dataset is selected fromthe reference group. The dataset includes patient data that is of asimilar type received from the patient monitoring device. A model of thereference group dataset is generated using a probability distributionfunction and automatically compared to the received physiological datato a model to derive an index for the patient. This method iscumbersome. For example, the method requires a model of the referencegroup dataset is generated and automatically compared using aprobability distribution function. Numerous other methods includevarious complexities such as U.S. Pat. No. 8,777,850 to Cho et al., USPregrant Application 2012/0109243 to Hettrick et al. U.S. Pat. No.7,682,316 B2 to Anderson et al.

While a number of methods can be used to predict HFH risk, improvementscan be made. For example, it is desirable to develop a method toestimate risk of HFH that can be easily implemented without undulyburdening healthcare providers. Additionally, it would be desirable tohave a method or system that was able to present increased gradations ofHFH risk instead of broad risk categories such as high risk and lowrisk.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual drawing illustrating an example system configuredto transmit diagnostic information indicative of heart failure thatincludes an implantable medical device (IMD) coupled to implantablemedical leads.

FIG. 2A is a conceptual drawing illustrating the example IMD and leadsof FIG. 1 in conjunction with a heart.

FIG. 2B is a conceptual drawing illustrating the example IMD of FIG. 1coupled to a different configuration of implantable medical leads inconjunction with a heart.

FIG. 3 is a functional block diagram illustrating an exampleconfiguration of the IMD of FIG. 1.

FIG. 4 is a functional block diagram illustrating an exampleconfiguration of an external programmer that facilitates usercommunication with the IMD.

FIG. 5 is a block diagram illustrating an example computer system thatincludes an external device and one or more computing devices that arecoupled to the IMD and programmer shown in FIG. 1 via a network.

FIG. 6 illustrates an example user interface that includes exemplaryheart failure data that may be used in determining heart failurehospitalization for a patient.

FIG. 7 illustrates an example user interface that includes exemplaryheart failure data that may be used in determining heart failurehospitalization for a patient.

FIG. 8 is a flow diagram of an exemplary technique heart failurepatient-related data organized and stored in memory that is subsequentlyused to predict prospective risk of heart failure hospitalization for apatient.

FIG. 9 depicts a diagnostic variable and an exemplary default thresholdvalue.

FIG. 10 depicts exemplary heart failure hospitalization risk assessmentsthat depend upon duration and amount of data observations for heartfailure patients.

FIGS. 11A-11B graphically depict heart failure hospitalization eventrates to number of diagnostic data observations in which impedance trendis excluded (FIG. 11A) and impedance trend is included (FIG. 11B).

FIG. 12 is a graphical user interface depicting device and clinicalevents cardiovascular alerts as associated with increasing risk.

FIGS. 13A-13C relate to formation of a database in which a lookup tablefor prospective heart failure risk is generated. FIG. 13A depicts a setof timelines in which data observations are triggered for a set ofpatients. FIG. 13B depicts a set of data observation categoriesassociated with heart failure hospitalizations for predictingprospective heart failure hospitalizations. FIG. 13C is a lookup tablecreated to associate total data observations during an evaluation periodwith prospective heart failure hospitalization.

FIG. 14 depicts a flow diagram that predicts prospective risk of heartfailure hospitalization for a patient.

FIG. 15 is a graphical user interface depicting device and clinicalevents cardiovascular alerts that can be displayed to a user.

DETAILED DESCRIPTION

Techniques are presented in which a medical device system, using data,customarily acquired through the use of an implantable medical device(IMD), predicts a patient's risk of heart failure hospitalization (HFH).The medical system includes an external device (e.g. server etc.) thatis accessed when predicting a patient's risk of HFH. The external devicehas a collection of heart failure patient-related data organized andstored in memory for access through a processor.

Multiple operations are involved in collecting patient data. Data isinterpreted to include datum, the singular form of data, or the pluralform of data. Data is typically collected from each patient through animplantable medical device or other suitable means. Techniques describedherein focus on data observations measured by the implantable medicaldevice and/or through other suitable means. Data observations is datathat crosses a parameter or metric threshold. The measured dataobservations are stored into the implantable medical device memory. Thedata is then subsequently transmitted and stored into the memory of theexternal device. Additionally, other data is transmitted and stored intomemory which includes whether or not a patient experienced HFH during anevaluation period. Whether the HFH occurred at the beginning or the endof the evaluation period is irrelevant to predicting the prospectiverisk of HFH. The technique described herein merely determines that a HFHoccurred sometime during the evaluation period.

After data is stored in the memory of the external device, the computersystem defines a look back period as a set of evaluation time periods.For example, the look back period for a patient includes two consecutiveevaluation periods—a preceding evaluation period and a currentevaluation period. The preceding evaluation period occurs immediatelybefore current evaluation period. In one or more embodiments, eachevaluation period extends the same amount of time (e.g., 30 days, 45days, 60 days, 75 days, 90 days etc.). In one or more other embodiments,evaluation periods may extend a different amount of time. For example,one evaluation period can be 30 days while another evaluation timeperiod may be 35 days. In still yet another embodiment, the precedingevaluation period can encompass a substantially different amount of timethan the current evaluation period (e.g. 90 days for the precedingevaluation period compared to 30 days for the current evaluationperiod). In one or more other embodiments, evaluation period couldencompass the entire duration between two consecutive follow-upsessions. Alternatively, the entire duration could be variable for thesame patient as time progresses. For example, entire duration betweenfollow-up 1 and follow-up 2 could be 60 days and the duration betweenfollow-up 2 and follow-up 3 could be 90 days.

Each evaluation period is categorized by its total amount of dataobservations experienced by that patient during that evaluation period.The total amount of data observations are counted without regard to thetype of data observations. To categorize or classify the evaluationperiod, data observations are counted to determine the total amount ofdata observations that occurred during that evaluation period. Forexample, if 0 data observations exist during the evaluation period, theevaluation period is designated as 0 data observations and theevaluation period is placed into the 0 data observations category. Acounter, associated with the zero data observations category, is thenincremented by “1” to indicate that the evaluation period has beendetermined to have zero data observations. During or after categorizingall of the evaluation periods, each evaluation period or evaluationwindow, within a particular data observations category, is counted.After determining a total amount evaluations periods that werecategorized as being within a data observations category (e.g. 0 dataobservations category, 1 data observations category, 2 data observationscategory, 3 data observations category etc.), the total amount is storedinto the memory of the external device.

At the same time or about the same time, a determination is made as towhether a HFH had occurred for each current evaluation periodexperienced by a patient. If a HFH was experienced by a patient duringthe current evaluation period, a HFH counter for that particular dataobservations category is incremented by “1.”

The risk of HFH is then estimated for each data observation category.For example, an equation for estimating HFH risk for each evaluationperiod, designated with 0, 1, 2, 3, or more data observations, is asfollows:

The prospective risk of HFH is then estimated for each data observationcategory. For example, the equation for estimating HFH risk for eachevaluation period, designated with 0, 1, 2, 3, or more dataobservations, is as follows:

$\frac{{{Number}\mspace{14mu} {of}\mspace{14mu} {Risk}\mspace{14mu} {Prediction}\mspace{14mu} {windows}\mspace{14mu} {with}} \geq {1\mspace{14mu} H\; F\; H}}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {Risk}\mspace{14mu} {Assessment}\mspace{14mu} {windows}}$

or, stated in another way, as follows:

HFH risk=(HFHnext)/Nnext

where HFHnext is the total amount of HFH that occurred during thecurrent prediction period (shown as “HFH” in FIG. 13B) for thatparticular data observations category while Nnext represents the totalnumber of evaluation windows (also referred to as “risk assessmentwindows” or “risk prediction windows”) that is associated with thatparticular data observations category.

Thereafter, a lookup table is created that associates total dataobservations during an evaluation period with prospective heart failurehospitalization. After the database has been completed and is stored inmemory, a patient's prospective risk of heart failure hospitalizationcan be estimated using the lookup table.

For example, patient data can be acquired through an implantable medicaldevice which indicates the patient experienced 2 data observationsduring a preceding evaluation period and 1 data observation for acurrent evaluation period. Using the total data observations, the lookuptable is accessed and the heart failure hospitalization risk isdetermined for each evaluation period. In one or more embodiments, aprospective heart failure hospitalization risk is determined by usingweighting factors in which the latter evaluation time period is weightedmore heavily than an earlier evaluation time period. In one or moreother embodiments, each evaluation period can be automatically weightedbased upon user-defined input.

In one or more other embodiments, a physician is able to obtain acustomized HFH risk for a patient by inputting data into the computerthat requires a new lookup table to be generated that solely associatesHFH patients' data with one or more characteristics of the physician'spatient. For example, a new lookup table could be generated in whichdata is limited to heart failure data acquired from patients that havecharacteristics shared with the physician's patient such as gender (i.e.data limited to women alone, men alone), age (e.g. pediatric patients)or some other age grouping (i.e. over 40, over 50, over 60, 40 to 50, 50to 60, 60 to 70, etc.) alone or other suitable categories. In one ormore other embodiments, the HFH risk can be further customized byconsidering one or two parameters that may be more relevant to thepatient's health history. For example, a physician may focus on a subsetof parameters that are found in the database. A graphical user interfacecan then be used to display the patient's prospective heart failurehospitalization risk to the user.

The present disclosure is configured to provide a more realistic HFHrisk than conventional methods. For example, in one or more embodiments,the prospective HFH risk is calculated by more heavily weighting themost recent evaluation period (i.e. current evaluation period) comparedto the evaluation period preceding the current evaluation time period.Yet another distinction is that the present disclosure providesincreased granular risk levels which also increases the accuracy ofestimating risk of HFH. By being able to more realistically predict apatient's HFH risk using presently available diagnostic data, thepatient or physician can act to minimize or potentially avoid a patientexperiencing HFH. For example, therapy can be adjusted in order to avoidHFH. Preventing HFH can potentially improve long-term patient outcomewhile reducing costs of care.

The present disclosure achieves numerous benefits over conventionalmethods. For example, skilled artisans will appreciate that the presentdisclosure is able to present increased gradations of HFH risk insteadof broad risk categories. Additionally, compared to conventionalmethods, the present disclosure easily estimates prospective risk of HFHwithout unduly burdening healthcare providers by merely requiring atotal count of data observations within an evaluation period.

FIG. 1 is a conceptual drawing illustrating an example system 10configured to transmit diagnostic information indicative of heartfailure of patient 14. In the example of FIG. 1, system 10 includes IMD16, which is coupled to leads 18, 20, and 22 and programmer 24. IMD 16may be, for example, an implantable pacemaker, cardioverter, and/ordefibrillator that provides electrical signals to heart 12 viaelectrodes coupled to one or more of leads 18, 20, and 22. Patient 14 isordinarily, but not necessarily a human patient.

In general, the techniques described in this disclosure may beimplemented by any medical device, e.g., implantable or external, thatsenses a signal indicative of cardiac activity, patient 14 activity,and/or fluid volume within patient 14. As one alternative example, thetechniques described herein may be implemented in an external cardiacmonitor that generates electrograms of heart 12 and detects thoracicfluid volumes, respiration, and/or cardiovascular pressure of patient14.

In the example of FIG. 1, leads 18, 20, 22 extend into the heart 12 ofpatient 14 to sense electrical activity of heart 12 and/or deliverelectrical stimulation to heart 12. Leads 18, 20, and 22 may also beused to detect a thoracic impedance indicative of fluid volume inpatient 14, respiration rates, sleep apnea, or other patient metrics.Respiration metrics, e.g., respiration rates, tidal volume, and sleepapnea, may also be detectable via an electrogram, e.g., based on asignal component in a cardiac electrogram that is associated withrespiration. In the example shown in FIG. 1, right ventricular (RV) lead18 extends through one or more veins (not shown), the superior vena cava(not shown), and right atrium 26, and into right ventricle 28. Leftventricular (LV) coronary sinus lead 20 extends through one or moreveins, the vena cava, right atrium 26, and into the coronary sinus 30 toa region adjacent to the free wall of left ventricle 32 of heart 12.Right atrial (RA) lead 22 extends through one or more veins and the venacava, and into the right atrium 26 of heart 12.

In some examples, system 10 may additionally or alternatively includeone or more leads or lead segments (not shown in FIG. 1) that deploy oneor more electrodes within the vena cava, or other veins. Furthermore, insome examples, system 10 may additionally or alternatively includetemporary or permanent epicardial or subcutaneous leads with electrodesimplanted outside of heart 12, instead of or in addition to transvenous,intracardiac leads 18, 20 and 22. Such leads may be used for one or moreof cardiac sensing, pacing, or cardioversion/defibrillation. Forexample, these electrodes may allow alternative electrical sensingconfigurations that provide improved or supplemental sensing in somepatients. In other examples, these other leads may be used to detectintrathoracic impedance as a patient metric for identifying a heartfailure risk or fluid retention levels.

IMD 16 may sense electrical signals attendant to the depolarization andrepolarization of heart 12 via electrodes (not shown in FIG. 1) coupledto at least one of the leads 18, 20, 22. In some examples, IMD 16provides pacing pulses to heart 12 based on the electrical signalssensed within heart 12. The configurations of electrodes used by IMD 16for sensing and pacing may be unipolar or bipolar. IMD 16 may detectarrhythmia of heart 12, such as tachycardia or fibrillation of the atria26 and 36 and/or ventricles 28 and 32, and may also providedefibrillation therapy and/or cardioversion therapy via electrodeslocated on at least one of the leads 18, 20, 22. In some examples, IMD16 may be programmed to deliver a progression of therapies, e.g., pulseswith increasing energy levels, until a fibrillation of heart 12 isstopped. IMD 16 may detect fibrillation employing one or morefibrillation detection techniques known in the art.

In addition, IMD 16 may monitor the electrical signals of heart 12 forpatient metrics stored in IMD 16 and/or used in generating the heartfailure risk level. IMD 16 may utilize two of any electrodes carried onleads 18, 20, 22 to generate electrograms of cardiac activity. In someexamples, IMD 16 may also use a housing electrode of IMD 16 (not shown)to generate electrograms and monitor cardiac activity. Although theseelectrograms may be used to monitor heart 12 for potential arrhythmiasand other disorders for therapy, the electrograms may also be used tomonitor the condition of heart 12. For example, IMD 16 may monitor heartrate (night time and day time), heart rate variability, ventricular oratrial intrinsic pacing rates, indicators of blood flow, or otherindicators of the ability of heart 12 to pump blood or the progressionof heart failure.

In some examples, IMD 16 may also use any two electrodes of leads 18,20, and 22 or the housing electrode to sense the intrathoracic impedanceof patient 14. As the tissues within the thoracic cavity of patient 14increase in fluid content, the impedance between two electrodes may alsochange. For example, the impedance between an RV coil electrode and thehousing electrode may be used to monitor changing intrathoracicimpedance.

IMD 16 may use intrathoracic impedance to create a fluid index. As thefluid index increases, more fluid is being retained within patient 14and heart 12 may be stressed to keep up with moving the greater amountof fluid. Therefore, this fluid index may be a patient metrictransmitted in diagnostic data or used to generate the heart failurerisk level. By monitoring the fluid index in addition to other patientmetrics, IMD 16 may be able to reduce the number of false positive heartfailure identifications relative to what might occur when monitoringonly one or two patient metrics. Furthermore, IMD 16, along with othernetworked computing devices described herein, may facilitate remotemonitoring of patient 14, e.g., monitoring by a health care professionalwhen the patient is not located in a healthcare facility or clinicassociated with the health care professional, during apost-hospitalization period. An example system for measuring thoracicimpedance and determining a fluid index is described in U.S. PatentPublication No. 2010/0030292 to Sarkar et al., entitled, “DETECTINGWORSENING HEART FAILURE BASED ON IMPEDANCE MEASUREMENTS,” whichpublished on Feb. 4, 2010 and is incorporated herein by reference in itsentirety.

IMD 16 may also communicate with external programmer 24. In someexamples, programmer 24 comprises an external device, e.g., a handheldcomputing device, computer workstation, or networked computing device.Programmer 24 may include a user interface that receives input from auser. In other examples, the user may also interact with programmer 24remotely via a networked computing device. The user may interact withprogrammer 24 to communicate with IMD 16. For example, the user mayinteract with programmer 24 to send an interrogation request andretrieve patient metrics or other diagnostic information from IMD 16. Auser may also interact with programmer 24 to program IMD 16, e.g.,select values for operational parameters of IMD 16. Although the user isa physician, technician, surgeon, electrophysiologist, or otherhealthcare professional, the user may be patient 14 in some examples.

For example, the user may use programmer 24 to retrieve information fromIMD 16 regarding patient metric data and/or the heart failure risklevel. Heart failure risk level may be transmitted as diagnosticinformation. Although programmer 24 may retrieve this information aftersubmitting an interrogation request, IMD 16 may push or transmit theheart failure risk level, for example, if the heart failure risk levelindicates a change in patient treatment is necessary. For example,gradations of risk level may be determined based on a total number oftimes that patient metrics exceed their representative thresholds.Additionally or alternatively, the risk level may be solely determinedby total number of data observations associated with one or more metricsover a pre-or post-specified time period.

IMD 16, external device 114, and/or programmer 24 may generate the HFHrisk level. Exemplary patient metric data may include intracardiac orintravascular pressure, activity, posture, respiration, thoracicimpedance, impedance trend etc. As another example, the user may useprogrammer 24 to retrieve information from IMD 16 regarding theperformance or integrity of IMD 16 or other components of system 10,such as leads 18, 20 and 22, or a power source of IMD 16. In someexamples, any of this information may be presented to the user as analert (e.g., a notification or instruction). Further, alerts may bepushed from IMD 16 to facilitate alert delivery whenever programmer 24is detectable by IMD 16. IMD 16 may wirelessly transmit alerts, or otherdiagnostic information, to facilitate immediate notification of theheart failure condition.

Programmer 24 may also allow the user to define how IMD 16 senses,detects, and manages each of the patient metrics. For example, the usermay define the frequency of sampling or the evaluation window used tomonitor the patient metrics. Additionally or alternatively, the user mayuse programmer 24 to set each metric threshold used to monitor thestatus of each patient metric. The metric thresholds may be used todetermine when one or more patient metrics has reached a magnitudeindicative of being at risk for heart failure and/or heart failurehospitalization. In some examples, when a data exceeds its respectivemetric threshold, the metric may be counted for that evaluation period.For example, if one or more patient metrics exceed their thresholds apredetermined number of times, the HFH risk level may be shown ingradations of increased risk level for patient 14 to be hospitalized,e.g. within thirty days. The HFH risk level is based upon apredetermined number of data observations. In other examples, thepredetermined number may be set to a different number or a risk levelpercentage (fraction). In this manner, the predetermined number isexceeded metrics thresholds. Programmer 24 may be used to set thispredetermined number or any other factors used to generate and interpretthe heart failure risk level.

IMD 16 and programmer 24 may communicate via wireless communicationusing any techniques known in the art. Examples of communicationtechniques may include, for example, radiofrequency (RF) telemetry, butother communication techniques such as magnetic coupling are alsocontemplated. In some examples, programmer 24 may include a programminghead that may be placed proximate to the body of the patient near theIMD 16 implant site in order to improve the quality or security ofcommunication between IMD 16 and programmer 24.

IMD 16 may automatically detect each of the patient metrics and storethem within the IMD for later transmission. Although IMD 16 mayautomatically detect a number (e.g. 10 or less) different patientmetrics in some examples, IMD 16 may detect more or less patient metricsin other examples. For example, the patient metrics may include two ormore of a thoracic fluid index, an atrial fibrillation duration, aventricular contraction rate during atrial fibrillation, a patientactivity, a nighttime heart rate, a heart rate variability, a cardiacresynchronization therapy (CRT) percentage (e.g., the percentage ofcardiac cycles for which cardiac resynchronization pacing was provided),or the occurrence of or number of therapeutic electrical shocks. Themetric-specific thresholds may include at least two of a thoracic fluidindex threshold of approximately 60, an atrial fibrillation durationthreshold of approximately 6 hours, a ventricular contraction ratethreshold approximately equal to 90 beats per minute for 24 hours, apatient activity threshold approximately equal to 1 hour per day forseven consecutive days, a nighttime heart rate threshold ofapproximately 85 beats per minute for seven consecutive days, a heartrate variability threshold of approximately 40 milliseconds for sevenconsecutive days, a cardiac resynchronization therapy percentagethreshold of 90 percent for five of seven consecutive days, or anelectrical shock threshold of 1 electrical shock. In addition totransmitting diagnostic information during a hospitalization period anda post-hospitalization period, IMD 16 may transmit diagnosticinformation to a clinician or other user prior to the hospitalizationperiod. In other words, IMD 16 may transmit a heart failure risk levelto a clinician before patient 14 is ever admitted to the hospital for aheart failure decompensation event. The risk level transmitted may besimilar to the post-hospitalization risk level, but, in some examples,the risk level transmitted prior to hospitalization may be transmittedless frequently, in response to an interrogation request from theclinician or other user, or upon the risk level reaching a more severelevel, e.g., a high or medium risk of hospitalization.

In addition, IMD 16 may alter the method with which patient metrics arestored within IMD 16. In other words, IMD 16 may store the automaticallydetected data observations with a dynamic data storage rate. Beforepatient 14 is admitted to the hospital, e.g., before the hospitalizationperiod, the clinician or admitting healthcare professional may submit aninterrogation request to IMD 16 in order to retrieve a portion of thestored patient metrics. The patient metrics may help the cliniciandetermine if hospitalization of patient 14 is a prudent action fortreatment. In response to the interrogation request, IMD 16 may transmitat least some of the automatically detected patient metrics stored inIMD 16.

FIG. 2A is a conceptual drawing illustrating IMD 16 and leads 18, 20,and 22 of system 10 in greater detail. As shown in FIG. 2A, IMD 16 iscoupled to leads 18, 20, and 22. Leads 18, 20, 22 may be electricallycoupled to a signal generator, e.g., stimulation generator, and asensing module of IMD 16 via connector block 34. In some examples,proximal ends of leads 18, 20, 22 may include electrical contacts thatelectrically couple to respective electrical contacts within connectorblock 34 of IMD 16. In addition, in some examples, leads 18, 20, 22 maybe mechanically coupled to connector block 34 with the aid of setscrews, connection pins, snap connectors, or another suitable mechanicalcoupling mechanism.

Each of the leads 18, 20, 22 includes an elongated insulative lead body,which may carry a number of concentric coiled conductors separated fromone another by tubular insulative sheaths. Bipolar electrodes 40 and 42are located adjacent to a distal end of lead 18 in right ventricle 28.In addition, bipolar electrodes 44 and 46 are located adjacent to adistal end of lead 20 in coronary sinus 30 and bipolar electrodes 48 and50 are located adjacent to a distal end of lead 22 in right atrium 26.In the illustrated example, there are no electrodes located in leftatrium 36. However, other examples may include electrodes in left atrium36.

Electrodes 40, 44, and 48 may take the form of ring electrodes, andelectrodes 42, 46 and 50 may take the form of extendable helix tipelectrodes mounted retractably within insulative electrode heads 52, 54and 56, respectively. In other examples, one or more of electrodes 42,46 and 50 may take the form of small circular electrodes at the tip of atined lead or other fixation element. Leads 18, 20, 22 also includeelongated electrodes 62, 64, 66, respectively, which may take the formof a coil. Each of the electrodes 40, 42, 44, 46, 48, 50, 62, 64 and 66may be electrically coupled to a respective one of the coiled conductorswithin the lead body of its associated lead 18, 20, 22, and therebycoupled to respective ones of the electrical contacts on the proximalend of leads 18, 20 and 22.

In some examples, as illustrated in FIG. 2A, IMD 16 includes one or morehousing electrodes, such as housing electrode 58, which may be formedintegrally with an outer surface of hermetically-sealed housing 60 ofIMD 16, or otherwise coupled to housing 60. In some examples, housingelectrode 58 is defined by an uninsulated portion of an outward facingportion of housing 60 of IMD 16. Other division between insulated anduninsulated portions of housing 60 may be employed to define two or morehousing electrodes. In some examples, housing electrode 58 comprisessubstantially all of housing 60. As described in further detail withreference to FIG. 3, housing 60 may enclose a signal generator thatgenerates therapeutic stimulation, such as cardiac pacing pulses anddefibrillation shocks, as well as a sensing module for monitoring therhythm of heart 12.

IMD 16 may sense electrical signals attendant to the depolarization andrepolarization of heart 12 via electrodes 40, 42, 44, 46, 48, 50, 62, 64and 66. The electrical signals are conducted to IMD 16 from theelectrodes via the respective leads 18, 20, 22. IMD 16 may sense suchelectrical signals via any bipolar combination of electrodes 40, 42, 44,46, 48, 50, 62, 64 and 66. Furthermore, any of the electrodes 40, 42,44, 46, 48, 50, 62, 64 and 66 may be used for unipolar sensing incombination with housing electrode 58. The combination of electrodesused for sensing may be referred to as a sensing configuration orelectrode vector.

In some examples, IMD 16 delivers pacing pulses via bipolar combinationsof electrodes 40, 42, 44, 46, 48 and 50 to produce depolarization ofcardiac tissue of heart 12. In some examples, IMD 16 delivers pacingpulses via any of electrodes 40, 42, 44, 46, 48 and 50 in combinationwith housing electrode 58 in a unipolar configuration. Furthermore, IMD16 may deliver defibrillation pulses to heart 12 via any combination ofelongated electrodes 62, 64, 66, and housing electrode 58. Electrodes58, 62, 64, 66 may also be used to deliver cardioversion pulses to heart12. Electrodes 62, 64, 66 may be fabricated from any suitableelectrically conductive material, such as, but not limited to, platinum,platinum alloy or other materials known to be usable in implantabledefibrillation electrodes. The combination of electrodes used fordelivery of stimulation or sensing, their associated conductors andconnectors, and any tissue or fluid between the electrodes, may definean electrical path.

The configuration of system 10 illustrated in FIGS. 1 and 2A is merelyone example. In other examples, a system may include epicardial leadsand/or subcutaneous electrodes instead of or in addition to thetransvenous leads 18, 20, 22 illustrated in FIG. 1. Further, IMD 16 neednot be implanted within patient 14. In examples in which IMD 16 is notimplanted in patient 14, IMD 16 may sense electrical signals and/ordeliver defibrillation pulses and other therapies to heart 12 viapercutaneous leads that extend through the skin of patient 14 to avariety of positions within or outside of heart 12. Further, externalelectrodes or other sensors may be used by IMD 16 to deliver therapy topatient 14 and/or sense and detect patient metrics used to generatediagnostic information, e.g., a heart failure risk level.

In addition, in other examples, a system may include any suitable numberof leads coupled to IMD 16, and each of the leads may extend to anylocation within or proximate to heart 12. For example, systems inaccordance with this disclosure may include three transvenous leadslocated as illustrated in FIGS. 1 and 2, and an additional lead locatedwithin or proximate to left atrium 36. As another example, systems mayinclude a single lead that extends from IMD 16 into right atrium 26 orright ventricle 28, or two leads that extend into a respective one ofthe right ventricle 26 and right atrium 26. An example of a two leadtype of system is shown in FIG. 2B. Any electrodes located on theseadditional leads may be used in sensing and/or stimulationconfigurations.

Any of electrodes 40, 42, 44, 46, 48, 50, 62, 64, 66, and 58 may beutilized by IMD 16 to sense or detect patient metrics used to generatethe heart failure risk level for patient 14. Typically, IMD 16 maydetect and collect patient metrics from those electrode vectors used totreat patient 14. For example, IMD 16 may derive an atrial fibrillationduration, heart rate, and heart rate variability metrics fromelectrograms generated to deliver pacing therapy. However, IMD 16 mayutilize other electrodes to detect these types of metrics from patient14 when other electrical signals may be more appropriate for therapy.

In addition to electrograms of cardiac signals, any of electrodes 40,42, 44, 46, 48, 50, 62, 64, 66, and 58 may be used to sense non-cardiacsignals. For example, two or more electrodes may be used to measure animpedance within the thoracic cavity of patient 14. Intrathoracicimpedance may be used to generate a fluid index patient metric thatindicates the amount of fluid building up within patient 14. Since agreater amount of fluid may indicate increased pumping loads on heart12, the fluid index may be used as an indicator of HFH risk. IMD 16 mayperiodically measure the intrathoracic impedance to identify a trend inthe fluid index over days, weeks, months, and even years of patientmonitoring. In general, the two electrodes used to measure theintrathoracic impedance may be located at two different positions withinthe chest of patient 14. For example, coil electrode 62 and housingelectrode 58 may be used as the sensing vector for intrathoracicimpedance because electrode 62 is located within RV 28 and housingelectrode 58 is located at the IMD 16 implant site generally in theupper chest region. However, other electrodes spanning multiple organsor tissues of patient 14 may also be used, e.g., an additional implantedelectrode used only for measuring thoracic impedance.

FIG. 2B is a conceptual diagram illustrating another example system 70,which is similar to system 10 of FIGS. 1 and 2A, but includes two leads18, 22, rather than three leads. Leads 18, 22 are implanted within rightventricle 28 and right atrium 26, respectively. System 70 shown in FIG.2B may be useful for physiological sensing and/or providing pacing,cardioversion, or other therapies to heart 12. Detection of patientdiagnostic data according to this disclosure may be performed in twolead systems in the manner described herein with respect to three leadsystems. In other examples, a system similar to systems 10 and 70 mayonly include one lead (e.g., any of leads 18, 20 or 22) to delivertherapy and/or sensor and detect patient metrics related to monitoringrisk of heart failure. Alternatively, diagnostic data may be implementedin systems utilizing subcutaneous leads, subcutaneous IMDs, or evenexternal medical devices. Although FIGS. 1-2 provide some useful IMD 16implantation examples, skilled artisans appreciate that IMD 16 and itsassociated electrodes can be implanted in other locations of the bodyand can include leads or be leadless.

FIG. 3 is a functional block diagram illustrating an exampleconfiguration of IMD 16. In the illustrated example, IMD 16 includes aprocessor 80, memory 82, metric detection module 92, signal generator84, sensing module 86, telemetry module 88, and power source 90. Memory82 includes computer-readable instructions that, when executed byprocessor 80, cause IMD 16 and processor 80 to perform various functionsattributed to IMD 16 and processor 80 herein. Memory 82 may include anyvolatile, non-volatile, magnetic, optical, or electrical media, such asa random access memory (RAM), read-only memory (ROM), non-volatile RAM(NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory,or any other digital or analog media.

Processor 80 may include any one or more of a microprocessor, acontroller, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), orequivalent discrete or analog logic circuitry. In some examples,processor 80 may include multiple components, such as any combination ofone or more microprocessors, one or more controllers, one or more DSPs,one or more ASICs, or one or more FPGAs, as well as other discrete orintegrated logic circuitry. The functions attributed to processor 80herein may be embodied as software, firmware, hardware or anycombination thereof.

Processor 80 controls signal generator 84 to deliver stimulation therapyto heart 12 according to a therapy parameters, which may be stored inmemory 82. For example, processor 80 may control signal generator 84 todeliver electrical pulses with the amplitudes, pulse widths, frequency,or electrode polarities specified by the therapy parameters.

Signal generator 84 is electrically coupled to electrodes 40, 42, 44,46, 48, 50, 58, 62, 64, and 66, e.g., via conductors of the respectivelead 18, 20, 22, or, in the case of housing electrode 58, via anelectrical conductor disposed within housing 60 of IMD 16. In theillustrated example, signal generator 84 is configured to generate anddeliver electrical stimulation therapy to heart 12. For example, signalgenerator 84 may deliver defibrillation shocks to heart 12 via at leasttwo electrodes 58, 62, 64, 66. Signal generator 84 may deliver pacingpulses via ring electrodes 40, 44, 48 coupled to leads 18, 20, and 22,respectively, and/or helical electrodes 42, 46, and 50 of leads 18, 20,and 22, respectively. In some examples, signal generator 84 deliverspacing, cardioversion, or defibrillation stimulation in the form ofelectrical pulses. In other examples, signal generator may deliver oneor more of these types of stimulation in the form of other signals, suchas sine waves, square waves, or other substantially continuous timesignals.

Signal generator 84 may include a switch module and processor 80 may usethe switch module to select, e.g., via a data/address bus, which of theavailable electrodes are used to deliver defibrillation pulses or pacingpulses. The switch module may include a switch array, switch matrix,multiplexer, or any other type of switching device suitable toselectively couple stimulation energy to selected electrodes.

Electrical sensing module 86 monitors signals from at least one ofelectrodes 40, 42, 44, 46, 48, 50, 58, 62, 64 or 66 in order to monitorelectrical activity of heart 12, impedance, or other electricalphenomenon. Sensing may be done to determine heart rates or heart ratevariability, or to detect arrhythmias or other electrical signals.Sensing module 86 may also include a switch module to select which ofthe available electrodes are used to sense the heart activity, dependingupon which electrode combination, or electrode vector, is used in thecurrent sensing configuration. In some examples, processor 80 may selectthe electrodes that function as sense electrodes, i.e., select thesensing configuration, via the switch module within sensing module 86.Sensing module 86 may include one or more detection channels, each ofwhich may be coupled to a selected electrode configuration for detectionof cardiac signals via that electrode configuration. Some detectionchannels may be configured to detect cardiac events, such as P- orR-waves, and provide indications of the occurrences of such events toprocessor 80, e.g., as described in U.S. Pat. No. 5,117,824 to Keimel etal., which issued on Jun. 2, 1992 and is entitled, “APPARATUS FORMONITORING ELECTRICAL PHYSIOLOGIC SIGNALS,” and is incorporated hereinby reference in its entirety. Processor 80 may control the functionalityof sensing module 86 by providing signals via a data/address bus.

Processor 80 may include a timing and control module, which may beembodied as hardware, firmware, software, or any combination thereof.The timing and control module may comprise a dedicated hardware circuit,such as an ASIC, separate from other processor 80 components, such as amicroprocessor, or a software module executed by a component ofprocessor 80, which may be a microprocessor or ASIC. The timing andcontrol module may implement programmable counters. If IMD 16 isconfigured to generate and deliver pacing pulses to heart 12, suchcounters may control the basic time intervals associated with DDD, VVI,DVI, VDD, AAI, DDI, DDDR, VVIR, DVIR, VDDR, AAIR, DDIR, CRT, and othermodes of pacing.

Intervals defined by the timing and control module within processor 80may include atrial and ventricular pacing escape intervals, refractoryperiods during which sensed P-waves and R-waves are ineffective torestart timing of the escape intervals, and the pulse widths of thepacing pulses. As another example, the timing and control module maywithhold sensing from one or more channels of sensing module 86 for atime interval during and after delivery of electrical stimulation toheart 12. The durations of these intervals may be determined byprocessor 80 in response to stored data in memory 82. The timing andcontrol module of processor 80 may also determine the amplitude of thecardiac pacing pulses.

Interval counters implemented by the timing and control module ofprocessor 80 may be reset upon sensing of R-waves and P-waves withdetection channels of sensing module 86. In examples in which IMD 16provides pacing, signal generator 84 may include pacer output circuitsthat are coupled, e.g., selectively by a switching module, to anycombination of electrodes 40, 42, 44, 46, 48, 50, 58, 62, or 66appropriate for delivery of a bipolar or unipolar pacing pulse to one ofthe chambers of heart 12. In such examples, processor 80 may reset theinterval counters upon the generation of pacing pulses by signalgenerator 84, and thereby control the basic timing of cardiac pacingfunctions, including anti-tachyarrhythmia pacing. The value of the countpresent in the interval counters when reset by sensed

R-waves and P-waves may be used by processor 80 to measure the durationsof R-R intervals, P-P intervals, P-R intervals and R-P intervals, whichare measurements that may be stored in memory 82. Processor 80 may usethe count in the interval counters to detect a tachyarrhythmia event,such as atrial fibrillation (AF), atrial tachycardia (AT), ventricularfibrillation (VF), or ventricular tachycardia (VT). These intervals mayalso be used to detect the overall heart rate, ventricular contractionrate, and heart rate variability. A portion of memory 82 may beconfigured as a plurality of recirculating buffers, capable of holdingseries of measured intervals, which may be analyzed by processor 80 inresponse to the occurrence of a pace or sense interrupt to determinewhether the patient's heart 12 is presently exhibiting atrial orventricular tachyarrhythmia.

In some examples, an arrhythmia detection method may include anysuitable tachyarrhythmia detection algorithms. In one example, processor80 may utilize all or a subset of the rule-based detection methodsdescribed in U.S. Pat. No. 5,545,186 to Olson et al., entitled,“PRIORITIZED RULE BASED METHOD AND APPARATUS FOR DIAGNOSIS AND TREATMENTOF ARRHYTHMIAS,” which issued on Aug. 13, 1996, or in U.S. Pat. No.5,755,736 to Gillberg et al., entitled, “PRIORITIZED RULE BASED METHODAND APPARATUS FOR DIAGNOSIS AND TREATMENT OF ARRHYTHMIAS,” which issuedon May 26, 1998. U.S. Pat. No. 5,545,186 to Olson et al. U.S. Pat. No.5,755,736 to Gillberg et al. is incorporated herein by reference intheir entireties. However, other arrhythmia detection methodologies mayalso be employed by processor 80 in other examples.

In some examples, processor 80 may determine that tachyarrhythmia hasoccurred by identification of shortened R-R (or P-P) interval lengths.Generally, processor 80 detects tachycardia when the interval lengthfalls below 220 milliseconds (ms) and fibrillation when the intervallength falls below 180 ms. These interval lengths are merely examples,and a user may define the interval lengths as desired, which may then bestored within memory 82. This interval length may need to be detectedfor a certain number of consecutive cycles, for a certain percentage ofcycles within a running window, or a running average for a certainnumber of cardiac cycles, as examples.

In the event that processor 80 detects an atrial or ventriculartachyarrhythmia based on signals from sensing module 86, and ananti-tachyarrhythmia pacing regimen is desired, timing intervals forcontrolling the generation of anti-tachyarrhythmia pacing therapies bysignal generator 84 may be loaded by processor 80 into the timing andcontrol module to control the operation of the escape interval counterstherein and to define refractory periods during which detection ofR-waves and P-waves is ineffective to restart the escape intervalcounters for the an anti-tachyarrhythmia pacing. Processor 80 detectsdata (e.g. data observations etc.) at an IMD 16 check and/orinterrogation time point. Data is sensed based on signals from sensingmodule 86. Additionally, cardioversion or defibrillation shock can bedetermined to be needed based upon sensed data, and processor 80 maycontrol the amplitude, form and timing of the shock delivered by signalgenerator 84.

Memory 82 is configured to store data. Exemplary data can be associatedwith a variety of operational parameters, therapy parameters, sensed anddetected data, and any other information related to the therapy andtreatment of patient 14. In the example of FIG. 3, memory 82 alsoincludes metric parameters 83 and metric data 85. Metric parameters 83may include all of the parameters and instructions required by processor80 and metric detection module 92 to sense and detect each of thepatient metrics used to generate the diagnostic information transmittedby IMD 16. Metric data 85 may store all of the data generated from thesensing and detecting of each patient metric. In this manner, memory 82stores a plurality of automatically detected patient metrics as the datarequired to generate a risk level of patient 14 being admitted to thehospital due to heart failure.

Metric parameters 83 may include definitions of each of the patientmetrics automatically sensed or measured by metric detection module 92.These definitions may include instructions regarding what electrodes orsensors to use in the detection of each metric. Preferred metricsinclude an (1) impedance trend index (also referred to as OPTIVOL®commercially available in IMDs from Medtronic Inc., located in MN), (2)intrathoracic impedance, (3) atrial tachycardia/atrial fibrillation(AT/AF) burden, (4) mean ventricular rate during AT/AF, (5) patientactivity, (6) V rate, (7) day and night heart rate, (8) percent CRTpacing, and/or (9) number of shocks. OPTIVOL® is described with respectto U.S. patent Ser. No. 10/727,008 filed on Dec. 3, 2003 issued as U.S.Pat. No. 7,986,994, and assigned to the assignee of the presentinvention, the disclosure of which is incorporated by reference in itsentirety herein. Other suitable metrics can also be used. For example, areference or baseline level impedance is established for a patient fromwhich subsequently acquired raw impedance data is compared. For example,raw impedance can be acquired from the electrodes (e.g. RV coil to Can)and compared to the reference impedance. Baseline impedance can bederived by averaging impedance over a duration of 7 (1-week) days to 90days (3-months).

Metric parameters 83 may also store a metric-specific threshold for eachof the patient metrics automatically detected by metric detection module92. Metric thresholds may be predetermined and held constant over theentire monitoring of patient 14. In some examples, however, metricthresholds may be modified by a user during therapy or processor 80 mayautomatically modify one or more metric thresholds to compensate forcertain patient conditions. For example, a heart rate threshold may bechanged over the course of monitoring if the normal or baseline heartrate has changed during therapy.

In one example, these metric-specific thresholds may include a thoracicfluid index threshold of approximately 60, an atrial fibrillation burdenthreshold of approximately 6 consecutive hours, a ventricularcontraction rate threshold approximately equal to 90 beats per minutefor 24 hours, a patient activity threshold approximately equal to 1 hourper day for seven consecutive days, a nighttime heart rate threshold ofapproximately 85 beats per minute for seven consecutive days, a heartrate variability threshold of approximately 40 milliseconds for sevenconsecutive days, a cardiac resynchronization therapy percentagethreshold of 90 percent for five of seven consecutive days, and anelectrical shock number threshold of 1 electrical shock. Thesethresholds may be different in other examples, and may be configured bya user, e.g., a clinician, for an individual patient.

Processor 80 may alter the method with which patient metrics are storedin memory 82 as metric data 85. In other words, processor 80 may storethe automatically detected patient metrics with a dynamic data storagerate. Metric detection module 92 may, for example, transmit diagnosticdata that is based on the patient metrics and whether any of the metricsexceed the respective specific metric thresholds. Any time that anautomatically detected patient metric exceeds their respective metricthreshold, the patient metric can be counted.

In this manner, metric detection module 92 may automatically detect eachof the patient metrics and store them within metric data 85 for latertransmission.

Example fluid index values and impedance measurements are described inU.S. Patent Application No. 2010/0030292 entitled “DETECTING WORSENINGHEART FAILURE BASED ON IMPEDANCE MEASUREMENTS,” which is incorporated byreference herein in its entirety. As the intrathoracic impedance remainslow, the fluid index may increase. Conversely, as the intrathoracicimpedance remains high, the fluid index may decrease. In this manner,the fluid index value maybe a numerical representation of retained fluidthat is specific to patient 14. In other examples, the intrathoracicimpedance may be alternatively used.

Metric data 85 is a portion of memory 82 that may store some or all ofthe patient metric data that is sensed and/or detected by metricdetection module 92. Metric data 85 may store the data for each metricon a rolling basis during an evaluation window. The evaluation windowmay only retain recent data and delete older data from the evaluationwindow when new data enters the evaluation window. In this manner, theevaluation window may include only recent data for a predeterminedperiod of time. In one or more other embodiments, memory can beconfigured for long term storage of data. Processor 80 may access metricdata when necessary to retrieve and transmit patient metric data and/orgenerate heart failure risk levels. In addition, metric data 85 maystore any and all data observations, heart failure risk levels or othergenerated information related to the heart failure risk of patient 14.The data stored in metric data 85 may be transmitted as part ofdiagnostic information. Although metric parameters 83 and/or metric data85 may consist of separate physical memories, these components maysimply be an allocated portion of the greater memory 82.

Metric detection module 92 may automatically sense and detect each ofthe patient metrics. Metric detection module 92 may then generatediagnostic data, e.g., data that indicates a threshold has been crossed,risk levels, based on the patient metrics. For example, metric detectionmodule 92 may measure the thoracic impedance, analyze an electrogram ofheart 12, monitor the electrical stimulation therapy delivered topatient 14, or sense the patient activity. It is noted that functionsattributed to metric detection module 92 herein may be embodied assoftware, firmware, hardware or any combination thereof. In someexamples, metric detection module 92 may at least partially be asoftware process executed by processor 80.

Metric detection module 92 may sense or detect any of the patientmetrics used as a basis for generating the heart failure risk level orotherwise indication of heart failure status or that patient 14 is atrisk for hospitalization. In one example, metric detection module 92 maycompare each of the patient metrics to their respective metric-specificthresholds defined in metric parameters 83 to generate the heart failurerisk level. Metric detection module 92 may automatically detect two ormore patient metrics. In other examples, metric detection module 92 maydetect different patient metrics.

In one example, metric detection module 92 may analyze electrogramsreceived from sensing module 86 to detect an atrial fibrillation oratrial tachycardia, and determine atrial tachycardia or fibrillationburden, e.g., duration, as well as a ventricular contraction rate duringatrial fibrillation. Metric detection module 92 may also analyzeelectrograms in conjunction with a real-time clock, patient posture oractivity signal, e.g., from activity sensor 96, and/or otherphysiological signals indicative of when a patient is asleep or awake todetermine a nighttime (or sleeping) heart rate or a daytime (or awake)heart rate or a difference between the day and night heart rate, andalso analyze electrograms to determine a heart rate variability, or anyother detectable cardiac events from one or more electrograms. Asdescribed above, metric detection module 92 may use peak detection,interval detection, or other methods to analyze the electrograms.

In addition, metric detection module 92 may include and/or controlimpedance module 94 and activity sensor 96. Impedance module 94 may beused to detect the thoracic impedance used to generate the thoracicfluid index. As described herein, impedance module 94 may utilize any ofthe electrodes of FIG. 1, 2 or 3 to take intrathoracic impedancemeasurements. In other examples, impedance module 94 may utilizeseparate electrodes coupled to IMD 16 or in wireless communication withtelemetry module 88. Once impedance module 94 measures the intrathoracicimpedance of patient 14, metric detection module 92 may generate thethoracic fluid index and compare the index to the thoracic fluid indexthreshold defined in metric parameters 83.

Activity sensor 96 may include one or more accelerometers or otherdevices capable of detecting motion and/or position of patient 14.Activity sensor 96 may therefore detect activities of patient 14 orpostures engaged by patient 14. Metric detection module 92 may, forexample, monitor the patient activity metric based on the magnitude orduration of each activity and compare the determined metric data to theactivity threshold defined in metric parameters 83. In addition todetecting events of patient 14, metric detection module 92 may alsodetect certain therapies delivered by signal generator 84, e.g., asdirected by processor 80. Metric detection module 92 may monitor signalsthrough signal generator 84 or receive therapy information directly fromprocessor 80 for the detection. Example patient metrics detected by thismethod may include a cardiac resynchronization therapy percentage ormetrics related to delivery of electrical shocks.

The cardiac resynchronization therapy (CRT) metric may be the amount orpercentage of time each day, or an amount of percentage of cardiaccycles, as examples, that IMD 16 delivers cardiac resynchronizationtherapy to heart 12. Low CRT amounts or percentages may indicate thatbeneficial therapy is not being effectively delivered and thatadjustment of therapy parameters, e.g., an atrioventricular delay or alower pacing rate, may improve therapy efficacy. In one example, higherCRT amounts or percentages may indicate that heart 12 is sufficientlypumping blood through the vasculature with the aid of therapy to preventfluid buildup. In examples of other types of cardiac pacing (non-CRT) orstimulation therapy, higher therapy percentages may indicate that heart12 is unable to keep up with blood flow requirements.

An electrical shock may be a defibrillation event or other high energyshock used to return heart 12 to a normal rhythm. The metric relatedelectrical shocks may be a number or frequency of electrical shocks,e.g., a number of shocks within a period of time. Metric detectionmodule 92 may detect these patient metrics as well and compare them to acardiac resynchronization therapy percentage and shock event threshold,respectively, defined in metric parameters 83 to determine when eachpatient metric has become critical. In one example, the electrical shockevent metric may become critical when a threshold number of shocks isdelivered, e.g., within a time period, or even when patient 14 evenreceives one therapeutic shock.

Metric detection module 92 may include additional sub-modules orsub-routines that detect and monitor other patient metrics used tomonitor patient 14 and/or generate the HFH risk level. In some examples,metric detection module 92, or portions thereof, may be incorporatedinto processor 80 or sensing module 86. In other examples, raw data usedto produce patient metric data may be stored in metric data 85 for laterprocessing or transmission to an external device. An external device maythen produce each patient metric from the raw data, e.g., electrogram orintrathoracic impedance. In other examples, metric detection module 92may additionally receive data from one or more implanted or externaldevices used to detect each metric which IMD 16 may store as metricdata.

In some examples, the patient metric thresholds used to generate therisk levels may change over time, e.g., the patient metric thresholdsmay either be modified by a user or automatically changed based on otherpatient conditions.

Telemetry module 88 may receive commands from programmer 24, forexample, to modify one or more metric parameters 83 (e.g., metriccreation instructions or metric-specific thresholds). In some examples,processor 80 may automatically adjust a metric-specific threshold ifcertain conditions are present in patient 14. For example, the thresholdmay be adjusted if patient 14 is experiencing certain arrhythmias ordata contained in cardiac electrograms change, e.g., there is adeviation in ST elevations or presence of pre-ventricular contractions,in such a manner that requires a change in the threshold.

Processor 80 may generate the HFH risk level based upon the patientmetrics sensed, detected, and stored in metric data 85 of memory 82. Forexample, processor 80 may continually update the HFH risk level asmetric detection module 92 updates each patient metric. In otherexamples, processor 80 may periodically update the HFH risk levelaccording to an updating schedule. In one or more other embodiments, thetotal number of data observations that exceed a threshold within apre-specified period of time can be used to determine the risk of heartfailure hospitalization.

As described above, processor 80 may provide an alert to a user, e.g.,of programmer 24, regarding the data from any patient metric and/or theHFH risk level. In one example, processor 80 may provide an alert withthe HFH risk level when programmer 24 or another device communicateswith IMD 16. Telemetry module 88 includes any suitable hardware,firmware, software or any combination thereof for communicating withanother device, such as programmer 24 (FIG. 1). Under the control ofprocessor 80, telemetry module 88 may receive downlink telemetry fromand send uplink telemetry to programmer 24 with the aid of an antenna,which may be internal and/or external. Processor 80 may provide the datato be uplinked to programmer 24 and the control signals for thetelemetry circuit within telemetry module 88, e.g., via an address/databus. In some examples, telemetry module 88 may provide received data toprocessor 80 via a multiplexer.

In some examples, processor 80 may transmit atrial and ventricular heartsignals, e.g., EGMs, produced by atrial and ventricular sense amplifiercircuits within sensing module 86 to programmer 24. Programmer 24 mayinterrogate IMD 16 to receive the heart signals. Processor 80 may storeheart signals within memory 82, and retrieve stored heart signals frommemory 82. Processor 80 may also generate and store marker codesindicative of different cardiac events that sensing module 86 detects,and transmit the marker codes to programmer 24. An example pacemakerwith marker-channel capability is described in U.S. Pat. No. 4,374,382to Markowitz, entitled, “MARKER CHANNEL TELEMETRY SYSTEM FOR A MEDICALDEVICE,” which issued on Feb. 15, 1983 and is incorporated herein byreference in its entirety.

In some examples, IMD 16 may signal programmer 24 to further communicatewith and pass the alert through a network such as the MedtronicCareLink® Network developed by Medtronic, Inc., of Minneapolis, Minn.,or some other network linking patient 14 to a clinician. In this manner,a computing device or user interface of the network may be the externalcomputing device that delivers the alert, e.g., patient metric data. Inother examples, one or more steps in the generation of the heart failurerisk level may occur within a device external of patient 14, e.g.,within programmer 24 or a server networked to programmer 24. In thismanner, IMD 16 may detect and store patient metrics before transmittingthe patient metrics to a different computing device.

In addition to transmitting diagnostic information during ahospitalization period and a post-hospitalization period, processor 80may control telemetry module 88 to transmit diagnostic information to aclinician or other user prior to the hospitalization period. If one ofthe automatically detected patient metrics exceeds its respectivemetric-specific threshold, processor 80 may control telemetry module totransmit that patient metric and possibly other patient metrics to allowthe clinician to more accurately diagnose the problem with patient 14.

FIG. 4 is a functional block diagram illustrating an exampleconfiguration of external programmer 24. As shown in FIG. 4, programmer24 may include a processor 100, memory 102, user interface 104,telemetry module 106, and power source 108. Programmer 24 may be adedicated hardware device with dedicated software for programming of IMD16. Alternatively, programmer 24 may be an off-the-shelf computingdevice running an application that enables programmer 24 to program IMD16.

A user may use programmer 24 to configure the operational parameters ofand retrieve data from IMD 16 (FIG. 1). The clinician may interact withprogrammer 24 via user interface 104, which may include display topresent graphical user interface to a user, and a keypad or anothermechanism for receiving input from a user. In addition, the user mayreceive an alert or notification from IMD 16 indicating the heartfailure risk level and/or patient metrics via programmer 24. In otherwords, programmer 24 may receive diagnostic information from IMD 16.

Processor 100 can take the form one or more microprocessors, DSPs,ASICs, FPGAs, programmable logic circuitry, or the like, and thefunctions attributed to processor 100 herein may be embodied ashardware, firmware, software or any combination thereof. Memory 102 maystore instructions that cause processor 100 to provide the functionalityascribed to programmer 24 herein, and information used by processor 100to provide the functionality ascribed to programmer 24 herein. Memory102 may include any fixed or removable magnetic, optical, or electricalmedia, such as RAM, ROM, CD-ROM, hard or floppy magnetic disks, EEPROM,or the like. Memory 102 may also include a removable memory portion thatmay be used to provide memory updates or increases in memory capacities.A removable memory may also allow patient data to be easily transferredto another computing device, or to be removed before programmer 24 isused to program therapy for another patient.

Programmer 24 may communicate wirelessly with IMD 16, such as using RFcommunication or proximal inductive interaction. This wirelesscommunication is possible through the use of telemetry module 106, whichmay be coupled to an internal antenna or an external antenna. Anexternal antenna that is coupled to programmer 24 may correspond to theprogramming head that may be placed over heart 12, as described abovewith reference to FIG. 1. Telemetry module 106 may be similar totelemetry module 88 of IMD 16 (FIG. 4).

Telemetry module 106 may also be configured to communicate with anothercomputing device via wireless communication techniques, or directcommunication through a wired connection. Examples of local wirelesscommunication techniques that may be employed to facilitatecommunication between programmer 24 and another computing device includeRF communication according to the 802.11 or Bluetooth specificationsets, infrared communication, e.g., according to the IrDA standard, orother standard or proprietary telemetry protocols. In this manner, otherexternal devices may be capable of communicating with programmer 24without needing to establish a secure wireless connection. An additionalcomputing device in communication with programmer 24 may be a networkeddevice such as a server capable of processing information retrieved fromIMD 16.

In this manner, telemetry module 106 may transmit an interrogationrequest to telemetry module 88 of IMD 16. Accordingly, telemetry module106 may receive data (e.g. diagnostic information etc.) or diagnosticinformation selected by the request or based on already entered patientstatus to IMD 16. The data may include patient metric values or otherdetailed information from telemetry module 88 of IMD 16. The data mayinclude an alert or notification of the heart failure risk level fromtelemetry module 88 of IMD 16. The alert may be automaticallytransmitted, or pushed, by IMD 16 when the heart failure risk levelbecomes critical. In addition, the alert may be a notification to ahealthcare professional, e.g., a clinician or nurse, of the risk leveland/or an instruction to patient 14 to seek medical treatment for thepotential heart failure condition that may require re-hospitalization isleft untreated. In response to receiving the alert, user interface 104may present the alert to the healthcare professional regarding the risklevel or present an instruction to patient 14 to seek medical treatment.

Either in response to heart failure data, e.g., the risk level orpatient metrics, or requested heart failure information, user interface104 may present the patient metrics and/or the heart failure risk levelto the user. In some examples, user interface 104 may also highlighteach of the patient metrics that have exceeded the respective one of theplurality of metric-specific thresholds. In this manner, the user mayquickly review those patient metrics that have contributed to theidentified heart failure risk level.

Upon receiving the alert via user interface 104, the user may alsointeract with user interface 104 to cancel the alert, forward the alert,retrieve data regarding the heart failure risk level (e.g., patientmetric data), modify the metric-specific thresholds used to determinethe risk level, or conduct any other action related to the treatment ofpatient 14. In some examples, the clinician may be able to review rawdata to diagnose any other problems with patient 14 or monitor theefficacy of treatments given to patient 14. For example, the clinicianmay check if the intrathoracic impedance has increased after diuretictherapy or if the heart rate has decreased during atrial fibrillation inresponse to a rate controlling drug. User interface 104 may even suggesttreatment along with the alert, e.g., certain drugs and doses, tominimize symptoms and tissue damage that could result from heartfailure. User interface 104 may also allow the user to specify the typeand timing of alerts based upon the severity or criticality of the heartfailure risk level. In addition to the heart failure risk level, inother examples, user interface 104 may also provide the underlyingpatient metrics to allow the clinician to monitor therapy efficacy andremaining patient conditions.

In some examples, processor 100 of programmer 24 and/or one or moreprocessors of one or more networked computers may perform all or aportion of the techniques described herein with respect to processor 80,metric detection module 92 and IMD 16. For example, processor 100 or ametric detection module 92 within programmer 24 may analyze patientmetrics to detect those metrics exceeding thresholds and to generate theheart failure risk level.

FIG. 5 is a block diagram illustrating an example system that includesan external device 114 (e.g. server, etc.), and one or more computingdevices 120A-120N, that are coupled to the IMD 16 and programmer 24shown in FIG. 1 via a network 112. Network 112 may be generally used totransmit diagnostic information (e.g., a risk level) from a remote IMD16 to another external computing device during a post-hospitalizationperiod. However, network 112 may also be used to transmit diagnosticinformation from IMD 16 to an external computing device within thehospital so that a clinician or other healthcare professional maymonitor patient 14. In this example, IMD 16 may use its telemetry module88 to communicate with programmer 24 via a first wireless connection,and to communication with an access point 110 via a second wirelessconnection. In the example of FIG. 5, access point 110, programmer 24,external device 114, and computing devices 120A-120N are interconnected,and able to communicate with each other, through network 112. In somecases, one or more of access point 110, programmer 24, external device114, and computing devices 120A-120N may be coupled to network 112through one or more wireless connections. IMD 16, programmer 24,external device 114, and computing devices 120A-120N may each compriseone or more processors, such as one or more microprocessors, DSPs,ASICs, FPGAs, programmable logic circuitry, or the like, that mayperform various functions and operations, such as those describedherein. Access point 110 may comprise a device that connects to network112 via any of a variety of connections, such as telephone dial-up,digital subscriber line (DSL), or cable modem connections. In otherexamples, access point 110 may be coupled to network 112 throughdifferent forms of connections, including wired or wireless connections.In some examples, access point 110 may be co-located with patient 14 andmay comprise one or more programming units and/or computing devices(e.g., one or more monitoring units) that may perform various functionsand operations described herein. For example, access point 110 mayinclude a home-monitoring unit that is co-located with patient 14 andthat may monitor the activity of IMD 16. In some examples, externaldevice 114 or computing devices 120 may control or perform any of thevarious functions or operations described herein, e.g., generate a heartfailure risk level based on the patient metric comparisons or createpatient metrics from the raw metric data. External device 114 furtherincludes input/output device 116, processor 118 and memory 119.Input/output device 116 includes input devices such as a keyboard, amouse, voice input etc. and output device includes graphical userinterfaces, printers and other suitable means. Processor 118 includesany suitable processor such as Intel Xeon Phi. Processor 118 isconfigured to set the start and end dates for each evaluation period.The evaluation period serves as an evaluation window that encompassesdata, acquired from each patient, that are within the boundaries (i.e.start and end times). Processor 118 is also configured to perform avariety of calculations. For example, processor 118 calculates risk ofHFH for each evaluation period. In one or more embodiments, weightingfactors are applied to two or more evaluations periods to determine theFrisk.

Memory 119 may include any volatile, non-volatile, magnetic, optical, orelectrical media, such as a random access memory (RAM), read-only memory(ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM(EEPROM), flash memory, or any other digital or analog media. Memory 119stores data. Exemplary data stored in memory 119 includes heart failurepatient data, heart failure prospective risk data etc. Evaluation periodstart and end times are also stored in memory. Heart failure patientdata includes data observations (e.g. data sensed from sensors thatcross a threshold). Additionally, evaluation period data is also storedin memory 119. For example, the start and end dates of the evaluationperiod data is stored in memory 119.

In some cases, external device 114 may be configured to provide a securestorage site for archival of diagnostic information (e.g., patientmetric data and/or heart failure risk levels) that has been collectedand generated from IMD 16 and/or programmer 24. Network 112 may comprisea local area network, wide area network, or global network, such as theInternet. In some cases, programmer 24 or external device 114 mayassemble the diagnostic data, heart failure data, prospective heartfailure risk data or other suitable data in web pages or other documentsfor viewing by and trained professionals, such as clinicians, viaviewing terminals associated with computing devices 120. The system ofFIG. 5 may be implemented, in some aspects, with general networktechnology and functionality similar to that provided by the MedtronicCareLink® Network developed by Medtronic, Inc., of Minneapolis, Minn.

In the manner of FIG. 5, computing device 120A or programmer 24, forexample, may be remote computing devices that receive and presentdiagnostic information transmitted from IMDs of multiple patients sothat a clinician may prioritize the patients needing treatmentimmediately. In other words, the clinician may triage patients byanalyzing the HFH levels of multiple patients. The computing device mayuse its communication module to receive the diagnostic information(e.g., heart failure data) transmitted from multiple IMDs via network112.

FIG. 6 illustrates an exemplary screen 130 of user interface 104 thatincludes diagnostic data. As shown in FIG. 6, screen 130 includes risklevel 144 that indicates the risk that patient 14 will be hospitalizeddue to heart failure. As described herein, the heart failure risk levelmay be indicative that patient 14 would be hospitalized for a first timeor hospitalized for another time (e.g., re-hospitalized or re-admitted).Although screen 130 is described as being presented on user interface104 of programmer 24, screen 130 may be presented on any user interfaceof any device used by a healthcare professional. The heart failurereport of screen 130 may be transmitted to a user at a scheduledfrequency, e.g., once a day or once a week, or in response to aninterrogation request from the user. As shown in FIG. 6, screen 130 is aheart failure report that includes identification data 132 and patienthistory data 134. Identification data 132 includes items such as thepatient name, the device name, the serial number of IMD 16, the date,and even the physician name. Patient history data 134 may be relevantdata that may help in the treatment of patient 14.

Screen 130 also includes clinical status 136 that includes informationregarding the stimulation therapy delivered by IMD 16. Screen 130 alsopresents trend summary 138. Trend summary 138 presents a snapshot ofcertain patient metrics that are exceeding their respective metricthresholds to contribute to the severity of risk level 144. Criticalindicator 140 is provided to remind the user that each of the patientmetrics with critical indicator 140 is contributing to the risk levelbecause the metric threshold has been met or exceeded. In examples inwhich risk level 144 is determined with a statistical analysis, criticalindicator 140 may not be necessary. However, certain patient metricsthat contribute significantly to the probability that patient 14 may bere-hospitalized may still be presented to the user.

In the example of FIG. 6, trend summary 138 presents four patientmetrics 142A, 142B, 142C, and 142D (collectively “patient metrics 142”).Thoracic fluid index metric 142A indicates a maximum detected value of96. Although thoracic fluid index metric 142A is not contributing torisk level 144 in this example, it is provided because it is animportant indicator of thoracic fluid volume and potential heartfailure. Atrial fibrillation duration 142B indicates that patient 14 hashad 28 days of atrial fibrillation or atrial tachycardia for 24 hours.Activity metric 142C indicates that patient 14 has been active for lessthan 1 hour per day for the last 4 weeks. In addition, ventricularpacing metric 142D (e.g., a cardiac resynchronization therapypercentage) indicates that IMD 16 has been pacing heart 12 less than 90percent of the time. As patient metrics 142 indicate, information may begiven that is more specific than just a threshold has been exceeded. Theactual observed patient metric data, or summary of the data, may bepresented in trend summary 138.

Risk level 144 is highlighted by a double-lined rectangle for easylocation by the user. In other examples, risk level 144 may stand outfrom the rest of screen 130 in different manners. For example, risklevel 144 may be of a different color, font size, or be presented withanimation (e.g., flashing or scrolling). Alternatively, risk level 144may be located at the top of screen 130 or other easily identifiablelocation. Although risk level 144 is generally presented as a wordcategory, risk level 144 may be presented with a fraction, percentage,weighted average, or other numerical score that indicates that theseverity of the risk level.

Although screen 130 may be a passively presented informational screen,screen 130 may be interactive. The user may select areas of screen 130to view more details about any of patient metrics 142, e.g., the usermay request diagnostic information from IMD 16. Screen 130, in otherexamples, may provide scroll bars, menus, and navigation buttons toallow the user to view additional information, adjust therapy, adjustmetric parameters, or perform other operations related to the treatmentof patient 14 with the patient metrics and risk level.

FIG. 7 illustrates an example screen 146 of another user interface 104with diagnostic data. Screen 146 may include data (e.g., raw orcalibrated data) from all of the patient metrics used to generate theheart failure risk level for patient 14. Although screen 146 isdescribed as being presented on user interface 104 of programmer 24,screen 130 may be presented on any user interface of any device used bya healthcare professional. As shown in FIG. 7, screen 146 providesanother heart failure report, similar to screen 130 of FIG. 6. Includedare the metric data for a variety of patient metrics 152, 154, 156, 158,160, 162, 164, and 166. Timeline 150 indicates for which months the datais representative in all the metric graphs. Although this four monthperiod may be the evaluation window, timeline 150 may cover manyevaluation windows. For example, the evaluation window may be equal toone week or one month, such that the risk level is reviewed after theevaluation window expires. In addition, the user may move through timewith an interactive timeline 150 in other examples. Although notpresented in screen 146, the heart failure risk level may also bepresented. In some examples, the user may select any point within thegraphs for the patient metrics to retrieve specific values of thepatient metric at that point in time.

Thoracic fluid index metric 152 is labeled as “Fluid Index.” Thoracicfluid index metric 152 illustrates that the thoracic fluid index hasbeen periodically raising and lowering over the months of May and June.In one example, the thoracic fluid index threshold may be approximately60. However, the thoracic fluid index threshold may be generally betweenapproximately 40 and 200.

Atrial fibrillation duration metric 154 is labeled “AF Duration” andindicates how many hours each day that the patient endured atrialfibrillation. As shown, atrial fibrillation duration metric 154 includescritical indicator 140 because of the days of atrial fibrillation shownat the end of June. An example atrial fibrillation duration thresholdmay be approximately 6 hours. However, the atrial fibrillation durationthreshold may be set generally between approximately 1 hour and 24hours.

Ventricular contraction metric 156 is labeled “AF+RVR” and indicates theventricular contraction rate during atrial fibrillation. The graph ofventricular contraction metric 156 provides the average ventricularcontraction rate for each day and also the maximum ventricularcontraction rate observed during each day. Generally, the ventricularcontraction rate during atrial fibrillation threshold may be set betweenapproximately 70 beats per minute and 120 beats per minute for 24 hours.In one example, the ventricular contraction rate threshold may beapproximately equal to 90 beats per minute for 24 hours. In otherexamples, the duration of 24 hours may be shorter or longer.

Activity metric 158 also is highlighted with critical indicator 140.Activity metric 158 is labeled “Activity” and indicates for how manyhours the patient is active each day. A patient may be considered activewhen, for example, the output of an accelerometer exceeds a threshold.Lower activity levels may be a risk factor for heart failure, and thegraph of activity metric 158 indicates that patient 14 has been lessactive at the end of June. In this manner, the patient metric ofactivity may be a metric where exceeding the metric-specific thresholdincludes dropping below the threshold. In one example, the patientactivity threshold may be approximately equal to 1 hour per day forseven consecutive days. In other examples, the threshold may be set tomore or less time over a different duration. Instead of hours per day,other examples of activity metric 158 may provide durations of certainpostures, e.g., lying down, sitting up, or standing. In general,activity metric 158 may include measurements of the rigor of patientactivity and/or the amount of time patient 14 is active.

Screen 148 also provides for heart rate metrics. Heart rate metric 160is labeled “HR” and indicates separate graphs for each of the nighttimeheart rate and daytime heart rate. In some examples, the nighttime heartrate may be more indicative of heart failure risk. Generally, thenighttime heart rate threshold may be set to between approximately 70beats per minute and 120 beats per minute for a certain period of time.In one example, the nighttime heart rate threshold may be approximately85 beats per minute for seven consecutive days. Heart rate variabilitymetric 162 is labeled “HR Variability” and indicates the degree ofchange in heart rate throughout the day. Since lower heart ratevariability may indicate an increased sympathetic tone detrimental toblood flow through the vasculature, heart rate variability may also be apatient metric where exceeding the metric-specific threshold includesdropping below the threshold. In one example, the heart rate variabilitythreshold may be set to approximately 40 milliseconds for sevenconsecutive days, but other variability thresholds may also be used. Inother examples, screen 148 may also provide comparisons between two ormore patient metrics, e.g., the difference between day heart rate andnighttime heart rate.

In addition, screen 148 may also provide a few patient metrics derivedfrom therapy delivered to patient 14. Therapy percentage metric 164 islabeled “% CRT” and indicates the percentage of time each day and nightthat IMD 16 is delivering a cardiac resynchronization therapy, e.g.,pacing therapy. Lower percentages of therapy may indicate diminishedblood flow through the vasculature. Generally, the cardiacresynchronization therapy percentage threshold may be set to a valuebetween 70 percent and 100 percent for a given period of time. In oneexample, the cardiac resynchronization therapy percentage threshold maybe set to approximately 90 percent for five of seven consecutive days.Since the nighttime therapy percentage is less than 90 percent, criticalindicator 140 is used to highlight therapy percentage metric 164.

In other examples, a ventricular pacing percentage may be monitored forpatients receiving pacing therapy with dual or single chamber pacingdevices. Increased ventricular pacing from single chamber cardiacresynchronization therapy devices may increase the risk of heart failurein some patients due to desynchronization of ventricular contractions inthe heart. Conversely, lower ventricular pacing in dual chamber devicesmay increase the risk of heart failure in some patients.

Further, shock metric 166 is labeled “Shocks” and indicates the numberof electrical shock events, e.g., cardioversion or defibrillation,endured by patient 14. As shown in FIG. 7, patient 14 has not beensubjected to any shock therapy. Although the threshold may be set to adifferent value, the electrical shock threshold may generally be set toapproximately 1 electrical shock.

Since each of patient metrics 154, 158, and 164 have exceeded theirrespective metric-specific threshold, critical indicator 140 is providedfor each metric.

In addition to, or in place of, critical indicators 140, patient metricsmay be highlighted with a different text color, circles or boxessurround each metric, or some other indication of the critical level ofeach metric. In other examples, other patient metrics may be presentedin heart failure metrics 148, e.g., blood pressure, blood glucose, lungvolume, lung density, or respiration rate, weight, sleep apnea burdenderived from respiration, temperature, ischemia burden, sensed cardiacevent intervals, and troponin and/or brain natriuretic peptide (BNP)levels.

Although screen 148 may be a passively presented informational screenwith diagnostic information, screen 148 may be interactive. The user mayselect areas of screen 148 to view more details about any of thepresented patient metrics, for example. The user may also move todifferent time periods with timeline 150. Screen 130, in other examples,may provide scroll bars, menus, and navigation buttons to allow the userto view additional information, adjust therapy, adjust metricparameters, or perform other operations related to the treatment ofpatient 14 with the patient metrics and risk level. Further, the usermay interact with the graph of each patient metric to expand the graphand view more details of the graph, perhaps even individual values.

In other examples, diagnostic information may be presented one patientmetric at a time or even raw data that IMD 16 uses to generate thepatient metric. For example, during a hospitalization period for patient14, IMD 16 may transmit the detected thoracic impedances to a remotecomputing device of a clinician treating patient 14. IMD 16 may transmitdetected thoracic impedances at a predetermined interval or in responseto an interrogation request from the clinician. The predeterminedinterval may be generally between approximately one minute and fourhours, but other predetermined intervals may be used. The clinician mayuse some or all of the diagnostic information to determine when patient14 has improved enough to be discharged from a hospital setting, orwhether patient 14 should be admitted to the hospital due to heartfailure.

Once the risk level is generated, processor 80 generates an alert of therisk level and transmits the alert to the user via telemetry module 88(244). As described herein, the alert may be transmitted on a scheduleor as soon as communication is possible to another device or accesspoint. In some examples, the heart failure risk level may only betransmitted when requested by a user. In some examples the alert mayalso include more detailed information regarding the patient metricsincluded in the risk level.

FIGS. 8-15 are directed to techniques that are able to morerealistically predict a patient's prospective risk of HFH. Bydetermining a patient's prospective HFH risk, medical personnel mayintervene with the heart failure patient's care to avoid or reduce thechances of a patient experiencing HFH. In order to predict a patient'sprospective risk of HFH, a heart failure patients' database is createdand stored into memory. The heart failure patients' database istypically based on real-time data stored in each patient's IMD 16, whichis then transmitted and stored into an external device's 114 memory 119.Data (e.g. a HFH that occurs during an evaluation period) is alsoacquired and stored into memory 119 when a patient communicates withmedical personnel and/or the information accessed from the IMD 16. Basedupon the real-time data obtained from each heart failure patient's IMD16 and whether or not the patient experienced a HFH, a lookup table isformed and stored into the database.

The flow diagram in FIG. 8 depicts an exemplary method 200 for creatinga database in which data, acquired from numerous heart failure patients,is stored into memory. To better understand the manner in which thedatabase is created, a process description is presented in which data isobtained from a single heart failure patient. The process in FIG. 8 isrepeatedly performed for each patient of the total amount of patientsfor the database. Data is typically acquired from the implantablemedical device and/or through communication between the patient andmedical personnel, all of which is transmitted and stored into thedatabase. For example, whether or not a patient experienced HFH duringan evaluation period is typically known since the patient's data recordswere stored into memory during the hospitalization. Whether the HFHoccurred at the beginning or the end of the evaluation period isirrelevant to predicting the prospective risk of HFH. The techniquedescribed herein merely determines that a HFH occurred sometime duringthe evaluation period.

Additionally, data, referred to as data observations, are associatedwith diagnostic parameters that are measured via one or more IMD 16sensors (e.g. electrodes, chemical sensors, etc.) disposed in apatient's body at block 202. A sensor acquires data that is compared toa threshold by a processor to determine whether a metric has beendetected. The detected data observation(s) are associated with a singlemetric or multiple metrics. Detected data is automatically stored intothe memory of IMD 16. Referring to FIG. 9, Table 1 presents exemplarydiagnostic parameter data and associated thresholds that may be used.Skilled artisans appreciate that the thresholds in Table 1 are examplesand may be established for a group of heart failure patients or can becustomized for each patient by a user of method 200. The left column ofTable 1 provides IMD parameters and associated representative trendwhile the right column presents an exemplary default threshold valuesthat correspond to each diagnostic metric. Each time a threshold for adiagnostic threshold is crossed, data is automatically stored intomemory, which can be accessed by the processor to generate IMD reportsto be viewed by a physician via a GUI or a printed IMD report.

Diagnostic metrics, typically indicative of worsening heart failure,mortality risk and/or hospitalization risk, include an (1) impedancetrend index (also referred to as OPTIVOL® commercially available in IMDsfrom Medtronic Inc., located in MN), (2) intrathoracic impedance, (3)atrial tachycardia/atrial fibrillation (AT/AF) burden, (4) meanventricular rate during AT/AF, (5) patient activity, (6) Ventricular (V)rate, (7) day and night heart rate, (8) percent CRT pacing, and/or (9)number of shocks. The OPTIVOL® index is an indicator of the amount offluid congestion experienced by the patient. The OPTIVOL® index is thedifference between an impedance measured during real time using IMD 16and a reference impedance, that can be continuously updated, establishedby the IMD 16 or during another visit to the physician.

OPTIVOL® is described in greater detail with respect to U.S. patent Ser.No. 10/727,008 filed on Dec. 3, 2003 issued as U.S. Pat. No. 7,986,994,and assigned to the assignee of the present invention, the disclosure ofwhich is incorporated by reference in its entirety herein.

Table 1 further depicts an exemplary impedance signal 300 compared to athreshold 306 over a several month time period (i.e. X-axis) with aY-axis extending from 0 to 160 ohms-days. Impedance was measuredintrathoracically across the right ventricular (RV) coil and the IMDhousing or can. The OPTIVOL® threshold 302 can be set at 60 ohms-days;however, the threshold can be set at another suitable value by the user.When the OPTIVOL® threshold crosses the threshold value, it signals thatthe patients may be at risk of congestion. The OPTIVOL® signal from thepatient appears to reach 160 ohms-days sometime in January 2009 as thereference impedance stays above the raw impedance, and then drops belowthe threshold thereafter as raw impedance goes higher than the referenceimpedance.

Impedance is yet another diagnostic parameter used to predict HFH.Impedance, as a diagnostic parameter, is measured against a baselinesignal 306 as a threshold level. Relative to the value of the baselinesignal 306, an increase in fluid volume and associated increase inwedge-pressure correlates with a reduction in intrathoracic impedance.In contrast, a decrease in fluid volume and associated decrease inwedge-pressure correlates with an increase in intrathoracic impedancerelative to the baseline value.

Another exemplary diagnostic parameter relates to AT/AF. Atrialfibrillation (AF) burden is measured as total duration of fast atrialrate during a 24-hour period associated with atrio-ventricularconduction ratio 2:1. Fast atrial rate is typically associated withrates 150 bpm or faster. About 60 bpm is normal for day-time and 40-50for night-time while 70 bpm is typically associated only with children.Likewise, if the patient has heart block then the atrial rate could beatat, for example, 90 bpm while the RV and LV maintain a normal rhythm at60 bpm. Fast atrial rate can also be defined as faster than the RV rate.

The AT/AF threshold indicates that a patient has AT/AF for a continuousminimum amount of time. For example, the AT/AF threshold is exceededwhen a patient experiences AT/AF for greater than 6 hours during a day.

Average ventricular rate during AT/AF (VRAF) threshold is yet anotherdiagnostic parameter that can be used to predict whether a patient isexperiencing worsening HF such that the patient is subject to increasedrisk of HFH. VRAF is typically measured during AF over a 24-hour timeperiod. The V rate can be defined as beats per minute (bpm) which is thetime from R-wave to R-wave. The V rate during AT/AF threshold isdesignated as a time period in AT/AF is greater than or equal to 6 hoursand a mean V-rate greater than 100 bpm.

Patient average activity is yet another useful diagnostic parameter forpredicting risk of HFH. Activity, a surrogate of functional capacity, isa quantitative measure of active duration during a pre-specified timeperiod. Lower activity (e.g. average activity for at least 1 week isless than 1 hour per day) can signal compromised functional capacity. Ifthe patient average activity is less than one hour per day over a week'speriod, the patient is at increased risk of worsening HF.

Yet another useful diagnostic parameter is V rate measured in beats perminute (bpm). V rate uses a threshold that relies on night heart rate(NHR). NHR, a marker of autonomic tone, can be associated with increasedrisk of HFH if the NHR is elevated. NHR measures the average restingheart rate between midnight and 4 AM. The threshold for NHR is greaterthan 85 bpm for the most recent seven days of data collection.

Heart rate variability (HRV), measured in milliseconds, and elevated NHRcan be used as markers of imbalance in autonomic tone. HRV less than50-60 ms is potentially a marker of elevated sympathetic tone and/orimbalance in sympathetic and parasympathetic tones. Still yet anotherexemplary parameter threshold is the percent pacing in which V pacingless than 90% since the last or previous session which is onlyapplicable to CRT pacing devices. V pacing is the amount of ventricularpacing delivered. The IMD also records other data such as percent CRTpacing delivered during a day, number of VT episodes, and whether thepatient received a defibrillation shock.

At block 202, patient data is acquired and stored into memory. Forexample, patient data can be transmitted and stored into memory 119 ofexternal device 114 via telemetry from an IMD 16. The patient data,obtained from clinical trials referred to as FAST and PARTNERS-HF,associates data observations with any HFHs (i.e. 220 HFHs) that may haveoccurred in a patient. FAST was a prospective double-blindedobservational study in patients (n=109) using Medtronic CRT-D or ICDdevices with EF≦35% and NYHA class III or IV. PARTNERS-HF was aprospective observational study enrolling patients (n=1024) with CRT-Ddevices with EF≦35%, NYHA class III or IV, and QRS duration≧130 ms.

Exemplary patient data used to create the database can be shown by theset of timeline data obtained from patients 1, 2, and 3, depicted inFIG. 13A. Each patient timeline shows data observations that occurredduring first and second time periods (i.e. FU_(X), FU_(X+1)) within alook back period. Patient 1 triggered data observations related to anactivity threshold (e.g. average activity for at least 1 week is lessthan 1 hour per day) during FU_(X). A defibrillation shock was deliveredto the patient during FU_(X+1). Accordingly, both FU_(X), FU_(X+1) forpatient 1 would be classified as evaluation periods that should beplaced in the 1 data observation category. Patient 2 triggered dataobservations related to OPTIVOL® during the first time period (i.e. FUx)while CRT, and NHR, were triggered in the next or second time period(i.e. FUx+1). Patient 3 triggered data observations related to CRTduring the first evaluation period, while OPTIVOL®, and activity weretriggered during the second evaluation period. Differentiation is notmade between type of data observation (e.g. activity, NHR, shock,OPTIVOL®, CRT etc.); therefore, any type of data observations triggeredduring an evaluation window is counted.

Patient data, shown in the timelines of FIG. 13A, is summarized in thecolumns (depicted in FIG. 13B) that represent 0, 1, 2, and more than 2data observations. For example, column 1 represents the zero dataobservations category from all patients. Each horizontal line in column1 indicates the duration for which 0 observations occurred for eachevaluation period. An evaluation period or window is placed or slottedinto column 1 if a determination is made that an evaluation window hadzero data observations. Column 1 also indicates that two of the tenevaluation periods included a HFH. Accordingly, the HFH risk for thezero observation category is 2/10 or 0.2, which is a 20% risk of HFH.

Column 2 is associated with FUs in which one (1) data observation hasbeen triggered, column 3 is associated with FUs in which two (2) dataobservations have been triggered, and column 3 is associated with FUs inwhich three (3) data observations have been triggered. HFH risk for eachof the observation categories can be computed by noting the proportionof all evaluation windows that has an HFH.

After data is stored in the database, the computer system defines a lookback period as a set of evaluation time periods at block 204 in FIG. 8.For example, the look back period for a patient includes two consecutiveevaluation periods—a preceding evaluation period and a currentevaluation period. In one or more embodiments, each evaluation periodextends the same amount of time (e.g., 30 days, 45 days, 60 days, 75days, 90 days etc.). In one or more other embodiments, evaluationperiods may extend a different amount of time, which provides a variablelook back period. For example, one evaluation period can be 30 dayswhile another evaluation time period may be 35 days. The user of method200 determines whether a variable or invariable evaluation period isused during the look back period.

Each evaluation period is categorized by total amount of dataobservations experienced by that patient during that evaluation periodat block 208. To categorize or classify the evaluation period, dataobservations (e.g. 0, 1, 2, 3, etc.) are counted at block 206. Forexample, if 0 data observations exist during the evaluation period, theevaluation period is designated as 0 data observations. A counter,associated with the zero data observations category, is then incrementedby “1” to indicate that the evaluation period has been determined tohave zero data observations. The zero data observations category countercontinues to be incremented when other evaluation periods have zero dataobservations.

During or after categorizing all of the evaluation periods, eachevaluation period, within a particular data observations category, isthen counted at block 210. After totaling evaluation periods that arewithin a data observations category (e.g. 0 data observations category,1 data observations category, 2 data observations category, 3 dataobservations category etc.), the total amount is stored into thedatabase. Once all of the evaluations periods have been processed, thecounting process is then terminated.

At the same time or about the same time, a determination is made as towhether a HFH had occurred for each evaluation period experienced by apatient at block 212. If a HFH was experienced by a patient during thecurrent evaluation period for a patient, a HFH counter for thatparticular data observations category is incremented by “1.”Additionally, the processor tracks the data observations category forwhich the HFH and the time period in which the HFH occurred.

The prospective risk of HFH is then estimated for each data observationcategory. For example, the equation for estimating HFH risk for eachevaluation period, designated with 0, 1, 2, 3, or more dataobservations, is as follows:

$\frac{{{Number}\mspace{14mu} {of}\mspace{14mu} {Risk}\mspace{14mu} {Prediction}\mspace{14mu} {windows}\mspace{14mu} {with}} \geq {1\mspace{14mu} H\; F\; H}}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {Risk}\mspace{14mu} {Assessment}\mspace{14mu} {windows}}$

or, stated in another way, as follows:

HFH risk=(HFHnext)/Nnext

where HFHnext is the total amount of HFH that occurred during thecurrent prediction period (shown as “HFH” in FIG. 13B) for thatparticular data observations category while Nnext represents the totalnumber of evaluation windows (also referred to as “risk assessmentwindows” or “risk prediction windows”) that is associated with thatparticular data observations category.

Thereafter, a lookup table (FIG. 13C) is created that associates totaldata observations during an evaluation period with prospective risk ofheart failure hospitalization shown as a decimal or a percentage. Afterpatient data from all of the heart failure patients have been processed,the database is deemed complete. Skilled artisans appreciate that whilethe database may be considered complete, the database can be configuredto be updated to include additional patient data or diagnostic data fromthe device.

After the database has been completed, a patient's prospective risk ofheart failure hospitalization can be estimated using the lookup tablestored into memory. For example, method 400 shown in FIG. 14, showsprospective HFH risk estimated for a patient. Patient data can beacquired through an IMD 16 or other suitable means at block 402. Atblock 404, a look back period is established. A look back period iscomprised of a set of consecutive evaluation periods. The set ofevaluation periods includes a first evaluation period (also known as apreceding evaluation period) and a second evaluation period (also knownas a current evaluation period) etc.).

Data observations that occurred during the preceding evaluation periodare counted to determine a total amount of data observations.Additionally, data observations are counted for the current evaluationperiod to determine a total amount of data observations. At block 408,each evaluation period is categorized based upon the total dataobservations. Using the total data observation category, the lookuptable is accessed by the processor. The processor searches for the totalobservation category within the lookup table and then determines theheart failure hospitalization risk associated with the totalobservations. The process of looking up each HFH risk from the look uptable is repeated for each evaluation period. In one or more embodiment,the HFH risk is determined for each evaluation period by the processorsearch for the risk percentage shown in the lookup table in FIG. 13.

In one or more embodiments, a prospective heart failure hospitalizationrisk is determined by using weighting factors. For example, weightingfactors can be used in which the latter evaluation time period isweighted more heavily than an earlier evaluation time period. Forexample, the weighting factor for a latest occurring evaluation timeperiod (i.e. current evaluation period) ranges from 0 up to 0.9. Aweighting factor for an evaluation time period (i.e. precedingevaluation period) ranges from 0 up to 0.5 compared to the latterevaluation period (i.e. current evaluation period). In one or more otherembodiments, weighting factors can be used to adjust for an evaluationperiod that extended for a shorter period of time compared to anotherevaluation period which was longer. For example, a short evaluationperiod (e.g. extended a total of 30 days) could be multiplied by 0.50(i.e. using a ratio equation, 30/60) of a long evaluation period of 60days). After determining the HF risk, a graphical user interfacedisplays the patient's prospective heart failure hospitalization risk tothe user.

Based upon the predicted HFH risk, a notification can be automaticallygenerated to the physician indicating that the patient has a substantialrisk of hospitalization and needs to be evaluated. Notifying thephysician to intervene can prevent or reduce HFH, which can potentiallyimprove long-term patient outcome while reducing costs of care.Additionally or alternatively, the physician can perform additionalclinical evaluation of the patient or the cardiac therapy can bewirelessly adjusted. In an alternate embodiment, the cardiac therapy isautomatically adjusted based upon pre-specified criteria determined atimplant or during a follow-up visit.

While determining the prospective HFH risk is important, the manner ofconveying information as efficiently as possible may save time for thephysician. FIGS. 10, 12, and 15 are exemplary graphical user interfacesthat can be displayed to a user. FIG. 10 provides a graphical userinterface that includes a set of exemplary HFH risk assessments for asingle patient over a longer period of time. The set of exemplary riskassessments are shown for each follow-up visit (FU1-FU4) over apre-specified time period (e.g. thirty day period). A follow-up visitoccurs at a single point in time whereas a time period between twoconsecutive follow-up visits is referred to as a risk evaluation windowor as a risk prediction window. Follow-up visit 1 (FU1) starts at timezero and ends thirty days after the start time. Follow-up visit 2 (FU2)starts at the end of FU1 and ends thirty days thereafter. Follow-upvisit 3 (FU3) starts at the end of FU2 and extends thirty days after thestart time of FU3. Follow-up visit 4 (FU4) starts at the end of FU3 andextends thirty days after the start time of FU4. The duration of therisk assessment windows for each follow-up session are shown by thehorizontal length of each risk assessment shown in FIG. 10.

The duration of the risk assessments for FU1 and FU2 is about the same,as is depicted by horizontal lengths of each risk assessment being aboutthe same in length. The risk assessment duration for FU4 is greater thanFU1 and FU2, while the FU3 risk assessment duration is substantiallygreater than all risk assessments shown in FIG. 10. Prospective riskassessments for HFH are shown by arrows, pointed from the end of each FUperiod to their respective start time, to reflect that the riskassessment depends on retrospective data. The retrospective data is thedata observations from that evaluation period. As previously stated,prospective HFH risk (i.e. the next 30 days, etc.) requires that theprocessor to look back to the evaluation period and determine the dataobservations experienced by the patient.

The look up table, stored in memory 119 of external device 114, isaccessed based upon the total data observations and then the prospectiveHFH risk is determined.

In an alternative embodiment, an evaluation is performed of alltriggered diagnostic data extending from time equal zero (e.g. implantof the IMD). While all current and most recent previous follow-ups startfrom time equals zero, each FU time period ends at a different time.Under this scenario, the risk prediction window extends from FU1 to FU2which is thirty days; the risk prediction window extends from FU2 to FU3which is 60 days; the risk prediction window extends from FU3 to FU4which is 90 days, and the risk prediction window extends from FU4 toanother point in time which may be 120 days. The embodiment thatincludes longer time periods may provide trend data that extends for agreater time period, which some physicians may consider to be useful. Inone or more embodiments, the physician can switch between graphical userinterfaces showing the most recent data alone or data which provideslonger trend information.

One or more embodiments of FIG. 10 can be applied such that, forexample, the current evaluation period extends from a time indicated bythe arrow head of FU2 to the arrow head of FU1. The preceding evaluationperiod extends from a time indicated by the arrow head of FU1 to thestart. In this example (shown in FIG. 10 in bold and capitalized text),the current and preceding evaluation periods are used to predict the 30day prospective risk immediately after the current evaluation period.

FIG. 12 is yet another user interface displaying data content to a user.The data content is parsed into device events and heart failure relatedevents. One user may be more interested in device events while anotheruser may be more interested in the heart failure related events. Thedevice events can be accessed by clicking on the tab with term “deviceevents” displayed thereon. The device events include, but are notlimited to, VF therapies off, a capture management warning, electricalreset, a warning about a lead issue, recommended replacement time forthe device, a charge circuit warning, VF detection therapy off, activecan (or housing) off, VT/VF detection disabled, wireless telemetry notavailable, SVC lead not detected.

FIG. 15 is yet another user interface that provides alerts when certainprescribed events occur relative to a patient. User interface tabs areprovided to allow the user to quickly access the data of most concern tothat user. The user interface displays tabs relate to overview data,alert groups, clinic management alerts (e.g. red, yellow and web-siteonly alerts), and notification hours. Red alert means a very importantthreshold has been crossed whereas a yellow alert means the item is ofconcern.

On the left hand side of the user interface is a list of items relatedto clinic management alerts. The list is parsed into data observationsand device items. The list of data observations includes AT/AF dailyburden greater than threshold, average ventricular rate during AT/AF,number of shocks delivered in an episode, and all therapies in the zoneexhausted. Multiple clinical events include time in AT/AF greater thanor equal to a predetermined number of hours per day, time in AT/AFgreater than or equal to a predetermined number of hours and mean V-rategreater than a prespecified level for a least one day, NHR greater than85 bpm for all or most of 7 days, average activity for at least one weekis less than 1 hour per day, at least 1

VT/VF shock, VP less than 90% since the last transmission (only visiblefor CRT devices), a prespecified number of events occurred from thelist, and user selected number (1-6 items) of events occurred. The usercould enter a number (e.g. 1-6 items), as shown, or a drop down menu canbe used to have the user select a number of items. The range of numbersprovided reflects how many events in the list have been checked by theuser.

Skilled artisans would appreciate that any number of boxes in FIG. 15can be checked or unchecked to indicate an event or alert has occurred.FIG. 15 illustrates only one possible scenario. For example, uncheckedevents include time in AT/AF [x] hours for at least 1 day, and NHR>85bpm for all of the most recent 7 days. Checked events include time inAT/AF X amount of pre-specified number of hours] hours and Mean V-Rate>yamount of pre-specified number of hours] for at least one day, averageactivity for at least 1 week is<1 hour/day, at least one VT/VF shock,and VP<90% since last transmission.

The device list includes lead/device integrity issues, VF detectiontherapy off, low battery voltage warning indicating a recommendedreplacement time for the device, excessive charge time for end ofservice, right ventricular lead integrity, right ventricular noise,atrial pacing impedance out of range, and right ventricular pacingimpedance out of range.

In order to avoid obscuring techniques of the invention, details as tothe database stored in memory 119 of external device 114 is presentedbelow. The HFH risk assessment effectiveness data was establishedthrough the use of two combined clinical studies referred to as FAST andPARTNERS. Table 2, presented below, summarizes the patient demographicsfor the 775 patients in FAST and PARTNERS trials that were used for thepresent analysis. All patients had CRT-D IMDs and the majority (87%) ofthe patients exhibited heart failure status of NYHA III. Additionally,the patients had similar characteristics of a patient populationreceiving cardiac resynchronization therapy.

Table 2 summarizes patient demographics in which FAST and PARTNERS-HFtrials were combined for the present analysis. The majority of patientswere classified as NYHA III with a mean age of 69. The majority ofpatients also experienced a variety of diseases (e.g. ischemic, coronaryartery disease, hypertension, diabetes etc.) typically associated withHF patients and take a variety of medications (e.g. ACE/ARB,beta-blockers, diuretics, digoxin, aldosterone etc.)

Table 2 summarizes demographic characteristics' of patients

Demographic Total characteristic (n = 775) Mean Age (SD)  69 (11) MaleGender 524 (68%) NYHA I  9 (1%) II  59 (8%) III 674 (87%) IV  33 (4%)Ischemic 485 (63%) Coronary Artery Disease 524 (68%) MyocardialInfarction 360 (46%) Hypertension 552 (71%) Diabetes 324 (42%) Historyof AF 219 (28%) LVEF ≦35% 676 (99%) Baseline Medications ACE/ARB 641(83%) Beta-Blockers 696 (90%) Diuretics 642 (83%) Digoxin 279 (36%)Aldosterone 257 (33%) AAD 138 (18%) Warfarin 183 (24%)

Tables 3-7 summarize some of the data from the clinical trials. Riskstratification is shown for 775 patients and 2276 follow-up sessions fornumerous IMD parameters, excluding impedance (Table 3) as compared todata that includes impedance (Table 4). Table 3 includes a number ofdata observations (i.e. IMD observations), a number of patients whoattend follow-up visits to be evaluated by a physician, a number of HFHsexperienced, percentage of HFHs, GEE adjusted HFHs (95% confidence level(CI)) and odds ratio versus 0 observation (95% CI).

As is evident from Table 3, the rate of HFH increased with increasingnumber of data observations. For example, when no IMD observations weretriggered, the 30-day event rate was 0.9% and increased to 13.6% forthree or more IMD observations triggered. A vast majority (˜71%) of thetotal follow-up sessions had no IMD observation. Additionally, theproportion of follow-up sessions with increasing observationsmonotonically decreased (23.5%, 4.3% and 1.3% for 1, 2 and observations,respectively).

Table 3 is performance of data observations excluding OPTIVOL® instratifying patients at the risk of heart failure hospitalization (HFH).

Number of GEE Odds Ratio Number of follow-ups adjusted versus 0 Device(Number of Number of HFHs (95% observation Observation(s) patients) HFHs(%) CI) (95% CI)   0 1614 (631) 14 (0.8%)  0.9% Reference    (0.5-1.6)group   1  535 (284) 17 (3.2%)  3.0%  3.6    (1.8-5.0) (1.6-7.8)   2  98(71)  7 (7.1%)  7.0%  8.5     (3.4-13.8) (3.3-22.3) ≧3  29 (24)  4(13.8%) 13.6% 17.9 (5.5-30.0) (5.6-57.2)

Univariate analysis for various parameters as a predictor of HFH isshown in Table 4. Diagnostic parameter data that was evaluated includedactivity, NHR, AF burden, VRAF, decrease in CRT pacing, shock, andOPTIVOL®. Data observations for each parameter are indicated to haveoccurred with a “yes” denoted in a particular row, while a lack of dataobservations are indicated by “no.” The number of FUs, and

HFHs that are associated with the data observations for a particularparameter is also provided. From the tabulated data, data observationspose an increased risk of HFH as opposed to lack of data observations.Additionally, the tabulated data shows that HFH rate varies from 4.6%(for decrease in CRT pacing) to 11.2% (for VRAF) for various parameters.For activity, AF burden, and V-pacing that triggered during a largeproportion of follow-up sessions (˜10% or more) the event rate was 5.1%,4.7% and 4.6%, respectively.

Table 4 univariate analysis and risk of various device parameters forHFH

Device Number of Number of Observation Follow-ups HFHs (%) GEE (95% CI)Activity Yes 277 14 (5.1%)  5.1% (3.0-8.4) No 1999 28 (1.4%)  1.4%(0.9-2.1) NHR   Yes 28  2 (7.1%)  7.2% (2.2-21.7) No 2248 40 (1.8%) 1.8% (1.3-2.5) AF   Burden Yes 235 11 (4.7%)  4.7% (2.5-8.5) No 2041 31(1.5%)  1.5% (1.0-2.2) VRAF Yes 26  3 (11.5%) 11.2% (3.8-28.5) No 225039 (1.7%)  1.7% (1.2-2.4) Decrease in   CRT Pacing Yes 228 11 (4.8%) 4.6% (2.4-8.4) No 2048 31 (1.5%)  1.5% (1.0-2.2) Shock   Yes 26  2(7.7%)  7.1% (1.5-27.3) No 2250 40 (1.8%)  1.8% (1.3-2.5) OPTIV   OL ®Yes 783 28 (3.6%)  3.5% (2.4-5.2) No 1493 14 (0.9%)  0.9% (0.6-1.6)

Risk stratification performance of various parameters including OPTIVOL®in 775 patients and 2276 follow-up sessions is shown in Table 4. Similarto the parameter set that excludes OPTIVOL®, the event rate of HFHincreased with increasing number of observations. The event rate for thescenario of no IMD observation was 0.4% and increased to 13.6% for threeof more observations.

Follow-up sessions with zero IMD observations constituted the largestproportion (48.5%) of all follow-up sessions and the proportion declinedwith increasing number of IMD observations (36.4%, 12.2% and 2.9% for 1,2 and 3 observations, respectively). In the univariate analysis, theOPTIVOL® index was found to have an HFH event rate of 3.5% as shown inTable 4.

Table 5 is performance of data observations including OPTIVOL® instratifying patients at the risk of heart failure hospitalization (HFH).

Number of Number of follow- Number of GEE adjusted Relative Risk Deviceups (Number of HFHs HFHs versus 0 Observation(s) patients) (%) (95% CI)observation   0 1103 (554)  4 (0.4%)  0.4% Reference (0.1-1.0) group   1 828 (514) 14 (1.7%)  1.7%  4.6   (0.9-3.0)  (1.4-14.5)   2  279 (190)15 (5.4%)  5.3% 14.9 (3.1-8.8)  (5.2-43.1) ≧3  66 (50)  9 (13.6%) 13.6%42.4 (7.2-24.3) (12.6-142.1)

FIGS. 11A-11B shows HFH event rate in the 30-days post-evaluation forthe IMD parameters excluding OPTIVOL® (FIG. 11A) and including OPTIVOL®(FIG. 11B). Kaplan Meier curves for 0, 1, 2, and 2 data observations areshown from time 0 to 30 days along the X-axis and HFH rate ofhospitalizations from 0% to 13% along the Y-axis after diagnosticevaluation for various number of data observations. The increase inevent rate with increasing number of observation is evident in FIGS.11A-11B.

Tables 6 and 7 show the sensitivity and specificity of method 200 inpredicting HFHs for the parameter set in which OPTIVOL® is included orexcluded from the data observations ranging from 1, 2 and For theparameter set excluding OPTIVOL®, the sensitivity for observation was68.9% and decreased to 9.5% for observations. The correspondingspecificity for observation was 71.2% and increased to 98.8% forobservations. Similarly, for the scenario when OPTIVOL® was included,the sensitivity for observations was 90.5% and decreased to 21.6% forobservations. The corresponding specificity increased from 49.1%observations) to 97.4% (3 observations). OPTIVOL® data included therelative increase in sensitivity for observations was significant (21.6%vs. 9.5%; see the bottom most rows in Tables 5 and 6) compared todecrease in specificity (97.4% vs. 98.8%).

TABLE 6 Sensitivity versus specificity in a 30-day evaluation frameworkfor 0, 1,2 and ≧3 observations for the parameter set excludingOPTIVOL ®. Sensitivity Specificity No. of GEE GEE adjusted Data adjusted(95% observations Unadjusted (95% CI) Unadjusted CI) ≧1 28/42 68.9%1600/2234 71.2% Observation(s) (66.7%) (52.8-81.5) (71.6%) (68.4-73.9)≧2 11/42 27.0% 2118/2234 94.5% Observations (26.2%) (15.2-43.3) (94.8%)(93.1-95.7) ≧3  4/42  9.5% 2209/2234 98.8% Observations  (9.5%) (3.7-22.5) (98.9%) (98.2-99.3)

TABLE 7 Sensitivity versus specificity in a 30-day evaluation frameworkfor 0, 1, 2 and ≧3 data observations for the parameter set includingOPTIVOL ®. Sensitivity Specificity GEE GEE No. of Data adjusted adjustedobservations Unadjusted (95% CI) Unadjusted (95% CI) ≧1 38/4 90.5%1099/2234 49.1% Observation(s) 2 (90.5%) (77.5-96.3) (49.2%) (46.5-51.7)≧2 24/4 58.0% 1913/2234 85.5% Observations 2 (57.1%) (42.0-72.4) (85.6%)(83.5-87.4) ≧3  9/42 21.6% 2177/2234 97.4% Observations (21.4%)(11.2-37.6) (97.4%) (96.5-98.1)

In addition, the look back period for assessing diagnostics was taken asthe entire duration between the follow-up periods to mirror real worldclinical practice. The risk of an HFH event with increasing number ofIMD observations. For example, a patient has 18 times to 42 timesincreased HFH risk when three or more data observations have occurredduring a FU time period compared to a patient with no observationdepending on whether the IMD parameter set excludes or includes OPTIVOL®as indicated by Tables 2 and 4, respectively. The sensitivity andspecificity for varying numbers of observations exhibits a trade-offbetween the two or more metrics such that as the specificity improveswith more stringent criterion of increasing numbers of observations thesensitivity worsens. For example, sensitivity with greater than or equalto three IMD observations is lower than ≧2 observations because a fewhospitalizations are not captured in greater than or equal to 3 IMDobservations i.e. they occur with lower number of IMD observations.Sensitivity and specificity for greater than or equal to 3 IMDobservations without OPTIVOL® are 9.5% and 98.8%, respectively. For anIMD with OPTIVOL®, the corresponding sensitivity improves substantiallyto 21.6% while the specificity drops slightly to 97.4%.

Since IMD data is continuously or periodically collected, HFH risk canbe predicted in real-time while in an ambulatory setting. For example,the patient can be assessed in an in-clinic setting. Alternatively, theIMD data can be transmitted to the clinic either automatically when aspecific alert (e.g. Medtronic, Inc. CAREALERT® in CARELINK SYSTEM®) hasoccurred or can be transmitted by the patient on a predeterminedschedule chosen by the clinician. Thus, a dynamic HF risk assessment canbe made available to the clinician that can then be used along withother clinical information to manage patient's HF.

Various IMD diagnostic parameters in the IMD are a reflection of variousunderlying physiological processes. Deviation of a given parameterbeyond a certain range may signal a compromise in physiologicalhomeostasis and hence be a marker of patient risk. For example,impedance is an indicator of fluid status. A drop in impedance andaccompanied rise in OPTIVOL® index is indicative of possible fluidoverload, while an excessive rise in impedance and drop in OPTIVOL®index might signal dehydration. Similarly, elevated NHR and abnormal HRVare potential markers of imbalance in autonomic tone, and lower activitycan signal compromised functional capacity. While each diagnosticparameter is a marker of risk, the prognostic value is improved whendiagnostic parameters are combined as shown by comparing Table 3 toTables 2 and 4.

One or more embodiments of the present disclosure employ an incrementalapproach to predicting prospective risk based solely on increasingnumber of observations such as 0, 1, 2, ≧3 data observations).Additionally, while a clinically relevant 30-day period is employed forHF risk prediction, the look back period for evaluating IMD diagnosticsdiffers in that all data is used between the previous and currentfollow-up sessions for the same patient. In one or more otherembodiments, the look back period for evaluating IMD diagnostic datadiffers in that all data is used between the previous and currentfollow-up sessions for similarly situated patients.

The present disclosure is advantageous to health care provider since thepresent disclosure does not require modifications to be implemented tothe threshold values from their default values. Implementing the presentdisclosure without modifying diagnostic parameters eases implementationof HFH risk since the physician does not need to modify the IMDthreshold. Compared to the intrathoracic impedance alone that is justone component among the set of IMD diagnostic parameters, a scheme usingmultiple diagnostic parameters improves overall diagnostic accuracy, asshown in Tables 3 and 4. The present disclosure provides dynamic andparticularly sensitive HFH risk assessments for ambulatory patientssolely using existing data observations parameters sensed through an IMD(CRT-D IMDs). Thresholds for various parameters and corresponding IMDobservations were unmodified from existing CRT-D IMDs.

Since the present disclosure relies on presently existing devicediagnostics observations, it is unnecessary to modify computerinstructions or configuration of the IMD in order to implement method200. Moreover, data can be acquired between two transmissions sent viatelemetry from the IMD to a physician, two in-clinic follow-up visits,or between a transmission sent via telemetry from the IMD and anin-clinic follow-up visit. Still yet another distinction is thatprospective HFH risk level is simply based on number of dataobservations triggered. For example, one data parameter or diagnostic isnot accorded greater weight from another diagnostic. Each data parameteris weighted the same as another data parameter. Accordingly, merelycounting the number of data observations during a shortened evaluationtime period (e.g. 30 days) is useful in order to calculate theprospective HF risk.

Various examples have been described that include determining apatient's prospective HFH risk using data solely obtained from real-timedata sensed using an IMD. These examples include techniques foridentifying patients with an elevated risk of being re-hospitalized dueto heart failure. In addition, an alert of patient risk levels may beremotely delivered to a healthcare professional from multiple differentpatients for triage and earlier diagnosis and treatment of heart failurebefore re-hospitalization. In one or more embodiments, the prospectiveHFH risk can be calculated for each shorter evaluation period in a setof shorter evaluation periods. The prospective HFH risk by determiningwhether a HFH event(s) occurred in the next 30 days (HFHnext) divided bythe total number of evaluations with or without HFH events in next 30days (Nnext). In yet another embodiment, a method of operation of amedical device system for determining prospective heart failurehospitalization risk is disclosed such that the method includesmeasuring one or more data observations via one or more electrodesassociated with an implanted medical device disposed in a patient'sbody, each data observation associated with a diagnostic parameter. Thedetected data observations is automatically stored into memory of theIMD 16 of a patient. A look back period is defined to include apreceding evaluation period and a current evaluation period. Each dataobservation is counted in a preceding evaluation period to determine atotal number of data observations over a pre-specified time. Adetermination is made as to whether a heart failure hospitalizationevent occurred in the current evaluation period. The heart failurehospitalization event of the current evaluation period is multiplied bythe total number of data observations over a pre-specified time period.The total number of evaluations in the current evaluation period isdetermined. The total number of evaluations in the current evaluationperiod is divided into the product for determining a prospective heartfailure hospitalization risk. Displayed on a graphical user interface isan overall evaluation period that extends from a preset start time andextends over the set of shorter evaluation periods. The prospectiveheart failure hospitalization risk for one shorter evaluation period isweighted differently from another shorter evaluation period from the setof evaluation periods. In one or more other embodiments, HFH risk iscalculated heart failure hospitalization (HFH) risk is calculated as anumber of evaluations with a given number of data observations in apreceding evaluation period and HFH (or no HFH) event in next 30 daysdivided by a total number of evaluations with HFH (without HFH) in next30 days.

In one or more embodiments, the HF risk is computed based upon totalnumber of IMD data observations. The HFH event rates and odds wereestimated using a generalized estimating equations (GEE) model for thegroups with different number of observations. For purposes of thispresent disclosure, no adjustment was made for baseline variables (e.g.age, gender, NYHA, history of coronary artery disease, MI, AF, diabetes,and hypertension) or baseline medications (e.g. ACE-I/ARB, diuretics,β-blockers, and anti-arrhythmic drugs).

The exemplary systems, methods, and interfaces described herein may beconfigured to assist a user (e.g., a physician, other medical personneletc.) in predicting a patient's risk of HFH. The medical device systemincludes an external device (e.g. server etc.) that is accessed whenpredicting a patient's risk of HFH. The external device has a collectionof heart failure patient-related data organized and stored in memory foraccess through a processor.

In one or more embodiments, the prospective risk of HFH is employed toswitch from a pacing therapy delivered by an implantable device toanother pacing therapy. For example, the pacing therapy can be switchedbetween CRT and fusion pacing. In one or more embodiments, the risk ofprospective HFH can cause the pacing therapy to be switched in onechamber to multisite pacing as described in Provisional Application No.62/152,684 filed Apr. 25, 2015 entitled METHOD FOR EFFICIENT DELIVERY OFMULTI-SITE PACING and incorporated by reference in its entirety.

The following paragraphs enumerated consecutively from 1 through 21provide for various aspects of the present disclosure. In one embodimentin a first paragraph (1) the present disclosure provides a method ofoperation of a medical device system for determining prospective heartfailure hospitalization risk, the method comprising:

1. One or more method of operation of a medical device system fordetermining prospective heart failure hospitalization risk, the methodcomprising:

-   -   (a) storing patients detected data observations into memory;    -   (b) defining a set of evaluation periods in which the detected        data observations occurred;    -   (c) counting detected data observations of an evaluation period        from the set of evaluation periods to determine a total amount        of detected data observations for the evaluation period;    -   (d) placing each evaluation period into a data observations        category in response to determining a total amount of detected        data observations;    -   (e) counting each evaluation period placed within the data        observations category to determine a total amount of evaluation        periods within the data observations category;    -   (f) counting heart failure hospitalizations (HFH) associated        with the data observations category to determine a total amount        of HFH;    -   (g) determining a ratio of the total amount of HFH to the total        amount of evaluation periods within the data observations        category;    -   (h) storing into memory each ratio for each data observations        category to form a lookup table;    -   (i) accessing from memory a heart failure patient's current and        preceding evaluation periods;    -   (j) counting detected data observations in the current and        preceding evaluation periods;    -   (k) using the lookup table to determine prospective heart        failure hospitalization risk for the preceding evaluation period        and the current evaluation period;    -   (l) determining a weighted prospective risk of HFH by        multiplying a weighting factor with the prospective heart        failure hospitalization risk for the preceding and current        evaluation period; and    -   (h) displaying on a graphical user interface the weighted        prospective risk of HFH.

2. A method of paragraph 1 further comprising:

-   -   selecting a weighting factor applied to be applied to the        preceding and the current evaluation periods.

3. A method of paragraph 1 or 2 wherein preceding evaluation periodbeing weighted differently than the current period.

4. A method of any of paragraphs 1-3, a smaller weighting factor appliedto the preceding evaluation period and a larger weighting factor beingapplied to the current evaluation period.

5. A method of any of paragraphs 3-4 wherein a weighting factor for alatest occurring evaluation time period is greater than a weightingfactor for an evaluation time period that occurs earlier than the latestoccurring evaluation time period.

6. A method of paragraph 5 wherein the weighting factor for the currentevaluation period ranges from 0 up to 0.9.

7. A method of any of paragraphs 5-6 wherein a weighting factor for thepreceding evaluation period ranges from 0 up to 0.5.

8. A method of any of paragraphs 1-7 wherein the heart failurehospitalization risk is predicted for a prospective time period of up to30 days.

9. A method of any of paragraphs 1-8 wherein the diagnostic parameterare one of an intrathoracic impedance, a thoracic fluid index, an atrialfibrillation duration after cardioversion therapy, a heart ratevariability, an elevation of ventricular rate during persistent atrialfibrillation, an elevation of night heart rate, and a cardiacresynchronization therapy percentage.

10. A method of any of paragraphs 1-9 further comprising:

-   -   totaling data observations during one of a current and preceding        evaluation period,    -   generating a data diagram depicting gradations of heart failure        hospitalization risk; and    -   displaying the gradations of heart failure hospitalization risk        on a graphical user interface in response to totaling number of        data observations.

11. A method of any of paragraphs 1-10 wherein the heart failurehospitalization risk can be determined without being in direct contactwith medical personnel.

12. A method of any of paragraphs 1-11 further comprising:

-   -   transmitting the detected data over a wireless connection to        medical personnel.

13. A method of any of paragraphs 1-12, further comprising:

-   -   comparing the heart failure hospitalization risk for a first        time period to another heart failure hospitalization risk of a        second time period; and    -   determining a trend of the heart failure hospitalization risk in        response to comparing the heart failure hospitalization risk for        the first and second time periods.

14. A method of any of paragraphs 1-13, wherein data observations occurover two data transmissions sent via telemetry.

15. A method of any of paragraphs 1-14, wherein data observations occurover one data transmission sent via telemetry and an in-clinicfollow-up.

16. A method of any of paragraphs 1-15, defining a look back period toinclude a preceding evaluation time period and a current evaluation timeperiod.

17. A method of any of paragraphs 1-16, wherein the current evaluationtime period being one of a data transmission or a follow-up clinicalvisit that occurs immediately after the preceding evaluation timeperiod.

18. A method of any of paragraphs 15-17, wherein a variable look backperiod is employed.

19. A method of any of paragraphs 15-18 wherein the variable look backperiod has no set duration.

20. A method of any of paragraphs 1-19 wherein 3 or more dataobservations provide from about 35 to about 45 times increased risk of aheart failure hospitalization in a next evaluation time period comparedto patients with zero data observations.

21. A method of any of paragraphs 1-20 wherein heart failurehospitalization (HFH) risk for a given number of observations iscalculated as HFH event in next 30 days divided by a total number ofevaluations with or without HFH (without HFH) in next 30 days.

22. A method of operation of a medical system for determiningprospective heart failure hospitalization risk, the method comprising:

-   -   (a) acquiring from a device memory a heart failure patient's        current and preceding evaluation periods;    -   (b) counting detected data observations in the current        evaluation period for a current evaluation total amount and        counting detected data observations in the preceding evaluation        period for a preceding evaluation period total amount;    -   (c) associating the current evaluation and preceding evaluation        total amounts with a lookup table to acquire prospective risk of        heart failure hospitalization (HFH) for the preceding evaluation        period and the current evaluation period;    -   (d) employing weighted sums of the prospective risk of HFH for        the preceding evaluation period and the current evaluation        period to calculate a weighted prospective risk of HFH for a        patient; and    -   (e) displaying on a graphical user interface the weighted        prospective risk of HFH for the patient.

23. The method of paragraph 22 wherein the lookup table comprises a setof data observations categories and for each said category a storedratio,

-   -   wherein each said data observations category defines a total        number of group data evaluation periods each having a defined        same number or falling within a same range of numbers of data        observations from a population of patients therein, and    -   wherein the stored ratio for each said data observations        category comprises a ratio of heart failure hospitalizations        associated with the said data observations category to the total        number of group evaluation periods within the said data        observation category.

24. The method of any of paragraphs 22-23 wherein an implanted device isused to obtain the data observations within one of the precedingevaluation period and the current evaluation period.

25. The method of any of paragraphs 22-24 further comprising:

-   -   using the prospective risk to modify therapy delivered by an        implantable device.

26. The method of any of paragraphs 22-25 further comprising:

-   -   using the prospective risk to switch from a pacing therapy        delivered by an implantable device to another pacing therapy.

27. The method of any of paragraphs 22-26 further comprising:

-   -   wherein another pacing therapy being one of biventricular pacing        and fusion pacing.

28. The method of any of paragraphs 22-27 further comprising:

-   -   using the prospective risk to modify therapy delivered by        administering an agent or adjusting an agent delivered to a        patient.

28. The method of any paragraph 28, wherein the agent is a drug.

29. The method of any of paragraphs 22-28 further comprising:

-   -   using the lookup table to prospectively evaluate patient HFH        risk based on observation category that is applicable to one of        the preceding evaluation period and the current evaluation        period.

30. A method of any of paragraphs 22-29 further comprising:

-   -   selecting a weighting factor to be applied to the preceding and        the current evaluation periods.

31. A method of claim 1 wherein the preceding evaluation period beingweighted differently than the current period.

32. A method of any of paragraphs 22-32, wherein a smaller weightingfactor being applied to the preceding evaluation period and a largerweighting factor being applied to the current evaluation period.

33. A method of any of paragraphs 22-33 wherein a weighting factor forthe current evaluation period ranges from 0 up to 0.9.

34. A method of any of paragraphs 22-33 wherein a weighting factor forthe preceding evaluation period ranges from 0 up to 0.5.

35. A method of any of paragraphs 22-34 wherein the heart failurehospitalization risk is predicted for a prospective time period of up to30 days.

36. A method of any of paragraphs 22-35 wherein the detected dataobservations are one of an intrathoracic impedance, a thoracic fluidindex, an atrial fibrillation duration after cardioversion therapy, aheart rate variability, an elevation of ventricular rate duringpersistent atrial fibrillation, an elevation of night heart rate, and acardiac resynchronization therapy percentage.

37. A method of operation of a medical system for determiningprospective heart failure hospitalization (HFH) risk, the methodcomprising:

-   -   (a) determining a number of an individual heart failure        patient's detected data observations during an individual        evaluation period;    -   (b) employing a lookup table to determine prospective heart        failure hospitalization risk based upon the said individual        evaluation period; and    -   (c) displaying on a graphical user interface the prospective        risk of HFH,        -   wherein the lookup table comprises a set of data            observations categories and for each said category a stored            ratio,        -   wherein each said data observations category defines a total            number of group data evaluation periods each having a            defined same number of data observations from a population            of patients therein; and        -   wherein the stored ratio for each said data observations            category comprises a ratio of heart failure hospitalizations            associated with the said data observations category to the            total number of group evaluation periods within the said            data observation category.

38. A medical system for determining prospective heart failurehospitalization risk, the system comprising:

-   -   (a) means for acquiring from a device memory a heart failure        patient's current and preceding evaluation periods;    -   (b) means for counting detected data observations in the current        evaluation period for a current evaluation total amount and        counting detected data observations in the preceding evaluation        period for a preceding evaluation period total amount;    -   (c) means for associating the current evaluation and preceding        evaluation total amounts with a lookup table to acquire        prospective risk of heart failure hospitalization (HFH) for the        preceding evaluation period and the current evaluation period;    -   (d) means for employing weighted sums of the prospective risk of        HFH for the preceding evaluation period and the current        evaluation period to calculate a weighted prospective risk of        HFH for a patient; and    -   (e) displaying on a graphical user interface the weighted        prospective risk of HFH for the patient.

39. The system of paragraph 38 wherein the lookup table comprises a setof data observations categories and for each said category a storedratio,

-   -   wherein each said data observations category defines a total        number of group data evaluation periods each having a defined        same number or falling within a same range of numbers of data        observations from a population of patients therein, and    -   wherein the stored ratio for each said data observations        category comprises a ratio of heart failure hospitalizations        associated with the said data observations category to the total        number of group evaluation periods within the said data        observation category.

40. The system of any of paragraphs 38-39 wherein an implanted device isused to obtain the data observations within one of the precedingevaluation period and the current evaluation period.

41. The system of any of paragraphs 38-40 further comprising:

-   -   means for using the prospective risk to modify therapy delivered        by an implantable device.

42. The system of any of paragraphs 38-41 further comprising:

-   -   means for using the prospective risk to switch from a pacing        therapy delivered by an implantable device to another pacing        therapy.

43. The system of any of paragraphs 38-42 further comprising:

-   -   using the prospective risk to modify therapy delivered by        administering an agent or adjusting an agent delivered to a        patient.

44. The system of any of paragraphs 38-43 further comprising:

-   -   using the lookup table to prospectively evaluate patient HFH        risk based on observation category that is applicable to one of        the preceding evaluation period and the current evaluation        period.

45. A method of operation of a medical system for determiningprospective heart failure hospitalization risk, the method comprising:

-   -   (a) acquiring from a device memory a heart failure patient's        current and preceding risk assessment periods;    -   (b) counting detected data observations in the current risk        assessment period for a current risk assessment total amount and        counting detected data observations in the preceding risk        assessment period for a preceding risk assessment period total        amount;    -   (c) associating the current risk assessment and preceding risk        assessment total amounts with a lookup table to acquire        prospective risk of heart failure hospitalization (HFH) for the        preceding risk assessment period and the current risk assessment        period;    -   (d) employing weighted sums of the prospective risk of HFH for        the preceding risk assessment period and the current risk        assessment period to calculate a weighted prospective risk of        HFH for a patient; and    -   (e) displaying on a graphical user interface the weighted        prospective risk of HFH for the patient.

The complete disclosures of the patents, patent documents, andpublications cited herein are incorporated by reference in theirentirety as if each were individually incorporated. Variousmodifications and alterations to this invention will become apparent tothose skilled in the art without departing from the scope and spirit ofthis invention. It should be understood that this invention is notintended to be unduly limited by the illustrative embodiments andexamples set forth herein and that such examples and embodiments arepresented by way of example only with the scope of the inventionintended to be limited only by the claims set forth herein as follows.

46. The method of paragraph 45 wherein prospective HFH risk can bepredicted in real-time while in an ambulatory setting.

1. A method of operation of a medical system for determining prospectiveheart failure hospitalization risk, the method comprising: (a) acquiringfrom a device memory a heart failure patient's current and precedingevaluation periods; (b) counting detected data observations in thecurrent evaluation period for a current evaluation total amount andcounting detected data observations in the preceding evaluation periodfor a preceding evaluation period total amount; (c) associating thecurrent evaluation and preceding evaluation total amounts with a lookuptable to acquire prospective risk of heart failure hospitalization (HFH)for the preceding evaluation period and the current evaluation period;(d) employing weighted sums of the prospective risk of HFH for thepreceding evaluation period and the current evaluation period tocalculate a weighted prospective risk of HFH for a patient; and (e)displaying on a graphical user interface the weighted prospective riskof HFH for the patient.
 2. The method of claim 1 wherein the lookuptable comprises a set of data observations categories and for each saidcategory a stored ratio, wherein each said data observations categorydefines a total number of group data evaluation periods each having adefined same number or falling within a same range of numbers of dataobservations from a population of patients therein, and wherein thestored ratio for each said data observations category comprises a ratioof heart failure hospitalizations associated with the said dataobservations category to the total number of group evaluation periodswithin the said data observation category.
 3. The method of claim 1wherein an implanted device is used to obtain the data observationswithin one of the preceding evaluation period and the current evaluationperiod.
 4. The method of claim 1 further comprising: using theprospective risk to modify therapy delivered by an implantable device.5. The method of claim 1 further comprising: using the prospective riskto switch from a pacing therapy delivered by an implantable device toanother pacing therapy.
 6. The method of claim 5 further comprising:wherein another pacing therapy being one of biventricular pacing, fusionpacing, multisite pacing during a single cardiac cycle along a singlechamber.
 7. The method of claim 1 further comprising: using theprospective risk to modify therapy delivered by administering an agentor adjusting an agent delivered to a patient.
 8. The method of claim 7,wherein the agent is a drug.
 9. The method of claim 1 furthercomprising: using the lookup table to prospectively evaluate patient HFHrisk based on observation category that is applicable to one of thepreceding evaluation period and the current evaluation period.
 10. Amethod of claim 1 further comprising: selecting a weighting factor to beapplied to the preceding and the current evaluation periods.
 11. Amethod of claim 1 wherein preceding evaluation period being weighteddifferently than the current period.
 12. A method of claim 1, wherein asmaller weighting factor being applied to the preceding evaluationperiod and a larger weighting factor being applied to the currentevaluation period.
 13. A method of claim 1 wherein a weighting factorfor the current evaluation period ranges from 0 up to 0.9.
 14. A methodof claim 1 wherein a weighting factor for the preceding evaluationperiod ranges from 0 up to 0.5.
 15. A method of claim 1 wherein theheart failure hospitalization risk is predicted for a prospective timeperiod of up to 30 days.
 16. A method of claim 1 wherein the detecteddata observations are one of an intrathoracic impedance, a thoracicfluid index, an atrial fibrillation duration after cardioversiontherapy, a heart rate variability, an elevation of ventricular rateduring persistent atrial fibrillation, an elevation of night heart rate,and a cardiac resynchronization therapy percentage.
 17. A method ofoperation of a medical system for determining prospective heart failurehospitalization (HFH) risk, the method comprising: (a) determining anumber of an individual heart failure patient's detected dataobservations during an individual evaluation period; (b) employing alookup table to determine prospective heart failure hospitalization riskbased upon the said individual evaluation period; and (c) displaying ona graphical user interface the prospective risk of HFH, wherein thelookup table comprises a set of data observations categories and foreach said category a stored ratio. wherein each said data observationscategory defines a total number of group data evaluation periods eachhaving a defined same number of data observations from a population ofpatients therein; and wherein the stored ratio for each said dataobservations category comprises a ratio of heart failurehospitalizations associated with the said data observations category tothe total number of group evaluation periods within the said dataobservation category.
 18. A medical system for determining prospectiveheart failure hospitalization risk, the system comprising: (a) means foracquiring from a device memory a heart failure patient's current andpreceding evaluation periods; (b) means for counting detected dataobservations in the current evaluation period for a current evaluationtotal amount and counting detected data observations in the precedingevaluation period for a preceding evaluation period total amount; (c)means for associating the current evaluation and preceding evaluationtotal amounts with a lookup table to acquire prospective risk of heartfailure hospitalization (HFH) for the preceding evaluation period andthe current evaluation period; (d) means for employing weighted sums ofthe prospective risk of HFH for the preceding evaluation period and thecurrent evaluation period to calculate a weighted prospective risk ofHFH for a patient; and (e) displaying on a graphical user interface theweighted prospective risk of HFH for the patient.
 19. The system ofclaim 18 wherein the lookup table comprises a set of data observationscategories and for each said category a stored ratio, wherein each saiddata observations category defines a total number of group dataevaluation periods each having a defined same number or falling within asame range of numbers of data observations from a population of patientstherein, and wherein the stored ratio for each said data observationscategory comprises a ratio of heart failure hospitalizations associatedwith the said data observations category to the total number of groupevaluation periods within the said data observation category.
 20. Thesystem of claim 18 wherein an implanted device is used to obtain thedata observations within one of the preceding evaluation period and thecurrent evaluation period.
 21. The system of claim 18 furthercomprising: means for using the prospective risk to modify therapydelivered by an implantable device.
 22. The system of claim 1 furthercomprising: means for using the prospective risk to switch from a pacingtherapy delivered by an implantable device to another pacing therapy.23. The system of claim 1 further comprising: using the prospective riskto modify therapy delivered by administering an agent or adjusting anagent delivered to a patient.
 24. The system of claim 1 furthercomprising: using the lookup table to prospectively evaluate patient HFHrisk based on observation category that is applicable to one of thepreceding evaluation period and the current evaluation period.
 25. Amethod of operation of a medical system for determining prospectiveheart failure hospitalization (HFH) risk, the method comprising: (a)determining a number of an individual heart failure patient's detecteddata observations during an individual evaluation period, the number ofdetected data observations being determined without regard as to a typeof detected data observations; (b) employing a lookup table to determineprospective heart failure hospitalization risk based upon the saidindividual evaluation period; and (c) displaying on a graphical userinterface the prospective risk of HFH, wherein the lookup tablecomprises a set of data observations categories and for each saidcategory a stored ratio. wherein each said data observations categorydefines a total number of group data evaluation periods each having adefined same number of data observations from a population of patientstherein.
 26. A method of claim 25 wherein the stored ratio for each saiddata observations category comprises a ratio of heart failurehospitalizations associated with the said data observations category tothe total number of group evaluation periods within the said dataobservation category.
 27. A method of operation of a medical system fordetermining prospective heart failure hospitalization risk, the methodcomprising: (a) acquiring from a device memory a heart failure patient'scurrent and preceding risk assessment periods; (b) counting detecteddata observations in the current risk assessment period for a currentrisk assessment total amount and counting detected data observations inthe preceding risk assessment period for a preceding risk assessmentperiod total amount; (c) associating the current risk assessment andpreceding risk assessment total amounts with a lookup table to acquireprospective risk of heart failure hospitalization (HFH) for thepreceding risk assessment period and the current risk assessment period;(d) employing weighted sums of the prospective risk of HFH for thepreceding risk assessment period and the current risk assessment periodto calculate a weighted prospective risk of HFH for a patient; and (e)displaying on a graphical user interface the weighted prospective riskof HFH for the patient.
 28. The method of claim 27 wherein prospectiveHFH risk can be predicted in real-time while in an ambulatory setting.